Synthetic data generation in computer-based reasoning systems

ABSTRACT

Techniques for synthetic data generation in computer-based reasoning systems are discussed and include receiving a request for generation of synthetic training data based on a set of training data cases. One or more focal training data cases are determined. For undetermined features (either all of them or those that are not subject to conditions), a value for the feature is determined based on the focal cases. In some embodiments, validity of the generated value may be checked based on feature information. In some embodiments, generated synthetic data may be checked against all or a portion of the training data to ensure that it is not overly similar.

PRIORITY INFORMATION

The present application is a continuation-in-part of U.S. applicationSer. No. 16/219,476 having a filing date of Dec. 13, 2018, which claimsthe benefit of U.S. Provisional Application Ser. No. 62/814,585 having afiling date of Mar. 6, 2019. Applicants claim priority to and benefit ofall such applications and incorporate all such applications herein byreference.

FIELD OF THE INVENTION

The present invention relates to computer-based reasoning systems andmore specifically to synthetic data in computer-based reasoning systems.

BACKGROUND

Computer-based reasoning systems can be used to predict outcomes basedon input data. For example, given a set of input data, aregression-based machine learning system can predict an outcome or makea decision. Computer-based reasoning systems will likely have beentrained on much training data in order to generate its reasoning model.It will then predict the outcome or make a decision based on thereasoning model.

One of the hardest problems for computer-based reasoning systems is,however, the acquisition of training data. Some systems may requiremillions or more sets of training data in order to properly train asystem. Further, even when the computer-based reasoning system hasenough data to use to train the computer-based reasoning system, thatdata may not be anonymous or anonymized in a way that satisfies userexpectation, terms of service, etc. Other systems require the rightsampling of training data. For example, even though a pump may spend 99%of its time in proper operating modes with similar data, acomputer-based reasoning system to control it may need significantlymore training on the potential failure scenarios with unusual data thatcomprise the other 1% of the operation time. Additionally, the trainingdata may not be appropriate for use in reinforcement learning becausesignificant amounts of data may be required in certain parts of theknowledge space or because the high costs associated with acquiring datasuch that the sampling process must be very selective.

The techniques herein overcome these issues.

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

SUMMARY

The claims provide a summary of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1A, FIG. 1B, and FIG. 1C are flow diagrams depicting exampleprocesses for synthetic data generation in computer-based reasoningsystems.

FIG. 2 is a block diagram depicting example systems for synthetic datageneration in computer-based reasoning systems.

FIG. 3 is a block diagram of example hardware for synthetic datageneration in computer-based reasoning systems.

FIG. 4 is a flow diagram depicting example processes for controllingsystems.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent invention.

General Overview

The techniques herein provide for synthetic data generation based incomputer-based reasoning systems. In some embodiments, thecomputer-based reasoning is a case-based reasoning system. As discussedelsewhere herein, computer-based reasoning systems need extensive, andoften specific training data. It can be prohibitively expensive and timeconsuming to create such training data. Further, in numerous situations,including in the context of reinforcement learning, the specifictraining data needed to properly train a computer-based reasoning modelmay be difficult or impossible to obtain. Training is often needed onrare, but important, scenarios, which are costly, difficult, ordangerous to create, and, as such, that training data may not beavailable. The techniques herein use existing training data and,optionally, target surprisal to create synthetic training data. In someembodiments, conditions may also be applied to the creation of thesynthetic data in order to ensure that training data meeting specificconditions is created. For undetermined features, a distribution for thefeature among the training cases is determined, and a value for thefeature is determined based on that distribution. In some embodiments,the values for some features are determined based at least in part onthe conditions or condition requirements that are placed on thesynthetic data. In some embodiments, the features that have conditionson them are called “conditioned features.” As used herein, the term“undetermined features” encompasses its plain and ordinary meaning,including, but not limited to those features for which a value has notyet been determined, and for which there is no condition or conditionalrequirement. For example, in those embodiments or instances where thereare no conditions on the synthesized data, all of the features mayinitially be undetermined features. After a value for a feature isdetermined, it is no longer an undetermined feature, but instead (asdescribed herein) may be used to as part of determining subsequentundetermined features.

In some embodiments, feature bounds associated with the feature can beused to generate the value for the feature (e.g., using the techniquesdiscussed herein). As particular examples, the feature value may besampled based on a uniform distribution, truncated normal, or any otherbounded distribution, between the feature bounds. In some embodiments,if the feature bounds are not used to generate the values, then thetechniques herein include checking feature bounds (if any have beenspecified, determined, or are otherwise known) of the value determinedfor the feature. In some embodiments, if feature bounds are known for aspecific feature, and the value determined for that feature is outsidethe feature bounds, then corrective action can be taken. Correctiveaction can include one or more of re-determining a value for thefeature, and optionally again checking the new value against the featurebounds, choosing a value within the range of the feature bounds,replacing the determined value with a new value determined based on adistribution just within the feature bounds (e.g., a uniform, normal, orother distribution within the feature bounds), and/or the like. As aparticular example, feature bounds may be used for time seriesvelocities (discussed elsewhere herein).

In many embodiments or contexts, it may be important for synthetictraining data to differ from the existing training data. For example, itmay be useful to have the synthetic data not contain identical datacases as the original training data or even data cases that are overly“similar” or “close” to original training data cases. As such, in theevent that synthetic training data is identical or too similar toexisting training data, the synthetic training case may be modified(e.g., resampled) and retested, or discarded. In some embodiments, thegenerated synthetic data may be compared against the at least a portionof the existing training data, and a determination may be made whetherto keep the synthetic training case based on the distance of thesynthetic training case to one or more elements of in the existingtraining data. For example, in some embodiments, each synthetic trainingcase generated using the techniques herein are compared to the existingtraining data in order to determine whether it is overly “similar” toexisting training data. Determining whether the synthetic data case isoverly similarity to a training case may be accomplished, in someembodiments, by determining the shortest distance between the syntheticdata case and the data cases in the training data. If the shortestdistance is below a certain threshold, then the synthetic training caseis deemed to be too “close” to an existing case. In the event that thesynthetic training case is overly similar to an existing training case,the synthetic case may be discarded, or values for one or more featuresmay be redetermined and then the modified synthetic data case may betested for similarity to the synthetic training cases. Any appropriatemeasure, metric, or premetric discussed herein may be used to determinethe “distance” or “closeness” of the synthetic case with other cases,including Euclidean distance, Minkowski distance, Damerau-Levenshteindistance, Kullback-Leibler divergence, 1—Kronecker delta, cosinesimilarity, Jaccard index, Tanimoto similarity, and/or any otherdistance measure, metric, pseudometric, premetric, index, etc. Further,the distance measure may be based on all of the features of the cases,or on a subset of the features. For example, the distance measure may bebased on a subset of features that, for example, are known to becombinable as identifiers of individuals. Additionally, in someembodiments, the closeness of synthetic data cases is determined as thesynthetic data cases are generated, after many or all of the syntheticdata cases are generated, or a combination of the two.

In some embodiments, the techniques may include determining thek-anonymity for synthetic data cases (e.g., either as the synthetic datacases are generated, after many or all the synthetic data cases aregenerated, or a combination of the two) (e.g., as part of determiningvalidity 152, fitness 160, and/or similarity 160 in FIGS. 1A, 1B, and/or1C). In some embodiments, determining the k-anonymity of a syntheticdata case may include determining whether there are k or more trainingdata cases that are “close” to the synthetic data case (e.g., within athreshold distance—e.g., a “similarity threshold”), and, if there are atleast k training data cases that are “close”, keeping the synthetic datacase because one would not be able to associate the synthetic data casewith less than k possible training data cases. In some embodiments, ifthere is at least one training data case, but fewer than k training datacases, that are within the similarity threshold of the synthetic datacase, then the synthetic training data case may be discarded, or valuesfor one or more features may be redetermined and then the modifiedsynthetic data case may be tested for similarity and/or k-anonymity tothe synthetic training cases. In some embodiments, even if there are kor more training data cases that are within the similarity thresholddistance of the synthetic data case, if one or more of the training datacases are within a closer threshold distance to the synthetic case, thenthe synthetic training data case may be discarded, or values for one ormore features or data elements in a time series may be redetermined andthen the modified synthetic data case may be tested for similarityand/or k-anonymity to the synthetic training cases. This may be useful,for example, to avoid having one of the k or more training data cases beoverly similar to the synthetic data case such that the synthetic datacase and one or more of the training data cases are or are nearlyidentical. For example, if a synthetic data case is identical to one ofthe training cases, even if there are k other training cases that aresimilar, it may still be useful to exclude that case.

The number k used in k anonymity may be set by the users of the systemor be automatically set based on desired anonymity, and may be relatedor unrelated to the “k” used for kNN searching. Further, the number kcan be any appropriate number, such as 1, 2, 9, 101, 1432, etc. Thenumber k can be the expected value of the number of data elements, theminimum number of elements that could be associated with a given point,or some other generalization of expectation including harmonic mean andgeometric mean.

In some embodiments, the distribution for a feature may be perturbedbased on target surprisal. In some embodiments, the distribution for afeature may be perturbed based on a multiplicity of surprisal orconfidence values, including surprisal or confidence related to modelresiduals and the similarity or distance to existing points. In someembodiments, generated synthetic data may be tested for fitness. In someembodiments, generated synthetic data may be used as a sampling processto obtain observations about unknown parts of the computer-basedreasoning model and to update the model based on the new informationobtained. In some embodiments, generated synthetic data may be used as asampling process that conditions the requests to increase the likelihoodof the system it is driving attaining a goal. Further, the generatedsynthetic data may be provided in response to a request, used to train acomputer-based reasoning model, and/or used to cause control of asystem.

Example Processes for Synthetic Data Generation

FIG. 1A is a flow diagram depicting example processes for synthetic datageneration in computer-based reasoning systems. In some embodiments,process 100 proceeds by receiving 110 a request for synthetic trainingdata. For example, a system or system operator may request additional ordifferent training data in order to train a computer-based reasoningthat will be used to control a system. In some cases, the system oroperator may request anonymous data that is similar to a currenttraining data set (or different from, but still anonymized). In othercases, the system or operator may require more data than is in thecurrent training data set, and therefore may request additional data toaugment the current training data set. In some cases, synthetic data maybe requested to direct sampling via a reinforcement learning process.The synthesized data (perhaps combined with original training data or byitself) may be used as part of a computer-based reasoning system tocause control of a system. Many controllable systems can be controlledwith the techniques herein, such as controllable machinery, autonomousvehicles, lab equipment, etc. In some embodiments, the request forsynthetic data may include a target surprisal for the target data. Insome embodiments, if low target surprisal is requested, then thesynthetic data may be close to and not differ much from existing data.If high target surprisal is requested, then the generated synthetic datamay differ much from the existing data.

The request can be received 110 in any appropriate manner, such as viaHTTP, HTTPS, FTP, FTPS, a remote procedure call, an API, a function orprocedure call, etc. The request can be formatted in any appropriateway, including in a structured format, such as HTML, XML, or aproprietary format or in a format acceptable by the API, remoteprocedure call, or function or procedure call. As one example, therequest may be received 110 by a training and analysis system 210 in themanner discussed above.

In some embodiments, optionally, the received 110 request for syntheticdata may also include one or more conditions for the synthetic data.These conditions may be restrictions on the generated synthetic data.For example, if the synthetic data being generated is for a checkersgame, a condition on the data may be that includes only moves that arepart of a winning strategy, that survive for at least S moves withoutlosing, and/or win within W moves. Another set of conditions on thesynthetic training data may be a particular board layout (e.g., thestarting checkers game state, the current checkers game state), etc.

When the received 110 request includes one or more conditions for thesynthetic data, the closest cases to the conditions may be determined120 as focal cases. In some embodiments, the closest cases to theconditions may be determined as the K nearest neighbors (KNN) for theconditions (e.g., the K cases that are “closest” to meeting theconditions). For example, if there are two features that haveconditions, A and B, and the conditions are A=3 and B=5, then the KNNfor the conditions would be those cases that are closest to meeting theconditions of A=3 and B=5. In some instances, if there are more than Kcases that fully meet the condition (e.g., there are more than K casesthat have feature values of A=3 and B=5, which scenario will be morecommon if the conditions are on features which are nominal orcategorical), then K cases may be selected from those cases meeting thecondition. These K cases may be selected from among those that fullymeet the conditions can be done randomly, or using any appropriatetechnique, such as by looking at the surprisal of those cases andchoosing the K with the highest (or lowest) surprisal, or all of the Kcases may be used. K may be 1, 2, 3, 5, 10, 100, a percentage of themodel, specified dynamically or locally within the model, or anyappropriate number. For distance measurements discussed herein (e.g.,for use with K nearest neighbors), any appropriate measure, metric, orpremetric may be used, including Euclidean distance, Minkowski distance,Damerau-Levenshtein distance, Kullback-Leibler divergence, 1—Kroneckerdelta, cosine similarity, Jaccard index, Tanimoto similarity, and/or anyother distance measure, metric, pseudometric, premetric, index, etc.

The conditions may be any appropriate single, multiple, and/orcombination of conditions. For example, individual values may be givenfor features (e.g., A=5 and B=3); ranges may be given (e.g., A>=5 andB<4); multiple values may be given (e.g., E=“cat”, “dog”, or “horse”);one or more combination can be given (e.g., [(A>1 and B<99) or (A=7 andE=“horse”)]).

The values for the conditioned features may be set or determined basedon the corresponding values for the features in the focal cases (e.g.,determined as the KNN of the conditions, as described above). Forexample, for each conditioned feature, the mean, mode, an interpolatedor extrapolated value, most-often occurring value of the correspondingfeature from among the focal cases may be chosen as the value for thefeature in the synthetic data case. In some embodiments, thedistribution of the values of the conditioned features in the focalcases may be calculated and a value may be chosen based on thatdistribution, which may include the maximum likelihood value, selectionvia random sampling, inverse distance weighting, kernel functions, orother function or learned metric. In some embodiments, the values forconditioned features are set to (or based on) the condition values (vs.the values for the conditioned feature in the focal cases as describedabove). For example, if the conditions are A−5 and B−3, then feature Amay be set to the value 5 and feature B may be set to the value 3regardless of the values of that feature in the focal cases.

When there are no conditions received 110 with the request for syntheticdata, a random case may be selected as a focal case or a set of randomcases may be selected as the set of focal cases. When there are noconditions, then, in some embodiments, the techniques begin by selectinga random case, selecting the first feature, or a random feature, or thenext feature prioritized by some metric, importance, conviction, orranking, and select the value from the selected case as the value of thefeature in the synthetic data value. Then, the techniques may proceed asdescribed. For example, in some embodiments, the value for a firstfeature (e.g., A=12) is chosen from the chosen case and then the KNN aredetermined 120. The KNN may be the K cases that are closest to havingthat value (e.g., A=12) are chosen as the focal cases. Additionally,other values computed from the data that are related to surprisal,confidence, or distance may be used in the selection of the focal cases(e.g., preferring the values to be chosen from areas where there isinsufficient data, or when combined with other surprisal metrics, toprefer values where there is not a lack of data but where the modelresiduals or uncertainty are high).

After the focal cases for the synthetic data have been determined 120(whether or not based on received 110 conditions), then a firstundetermined feature is selected 130. When there are no conditions,selecting 130 the first undetermined feature comprises selecting 130 oneof the features from the randomly selected case that was not previouslydetermined. When there are conditions on the synthetic data, then theconditioned features are first set based on the conditions and the focalcases that are KNN of the condition (as described elsewhere herein).After the first feature(s) have been determined (whether or not thereare conditions), then the next (undetermined) feature may be selected.Selecting 130 which undetermined feature to determine next can be donein any appropriate manner, such as selecting randomly among theremaining undetermined features, choosing the feature with the highestor lowest conviction, etc.

The distribution of values for the undetermined feature is thendetermined 140. For example, the distribution may be assumed to be lognormal, Laplace, Gaussian, normal, or any other appropriatedistribution, and be centered, e.g., on the computed undeterminedfeature value or on the median or mode or weighted sample or selection(e.g., weighted by probability, inverse distance, frequency, or othermeasure of likelihood) of the values for the undetermined feature in theset of focal cases (or in the training data). The distribution for thefeature can also be determined by parameterizing it via surprisal usingthe distribution's entropy. For example, if a distribution has an error,σ, with the error modeled as a gaussian distribution, and we know thatthe entropy of a sample from gaussian distribution is ½ log(2 π e σ²),we can adjust the error parameter to match a specified level ofsurprisal for that feature when taking a sample of the feature as thesynthesized value. Alternatively, surprisal may also be determined bymeasuring other types of information, such as Kullback-LeiblerDivergence (“KL divergence” or “Div_(KL)(x)”) or cross entropy, and adesired surprisal can be attained by adjusting the correspondingparameters for the distribution. Methods describing distance from apoint as a probability can be used to map the surprisal to distance, andmay include any relevant distribution. When synthesizing data formultiple features, each feature can be set to the same surprisal, oralternatively each feature can “use up” the surprisal budget for thesynthetic data parameterizing each feature's distribution with its ownamount of surprisal, treating total surprisal of the synthesized data asa budget or goal for all of the features together. Some features may beaccorded more surprisal than others, and therefore may “use up” more ofthe surprisal budget. In cases where higher surprisal is desired,distributions will typically be wider. In situations where lowersurprisal is desired, distributions will typically be narrower. Therelative surprisal accorded each feature may be set or determined in anyappropriate manner, including assigning the relative amount of surprisalrandomly, having the relative amounts set by a human operator, and/orsetting them based on a particular measure or metric, such as having thefeatures with the lowest (or highest) surprisal in the training databeing accorded more of the surprisal budget. Extensive additionaldiscussion of these techniques are given elsewhere herein.

The value of for the undetermined feature for the synthetic case maythen be determined 150 based on the determined 140 distribution.Determining the value based on the determined 140 distribution comprisesselecting a value (or sampling) randomly based on a random number andthe determined 140 distribution. In some embodiments, this is performedvia inverse transform sampling. As one example, a random valuerepresenting the 3^(rd) percentile of the distribution of the randomvariable would translate to a value in the 3^(rd) percentile of thedistribution, and a uniformly generated random number may be transformedinto the distribution by the inverse cumulative density function. Insome embodiments, the distribution does not have a closed form solutionto translate a uniformly chosen random number to a random number fromthe parameterized distribution, and techniques to generate the requiredrandom number include rejection sampling, the Box-Muller transform, andthe Ziggurat algorithm. As denoted by the dotted line from determining150 and selecting 130, the process 100 may continue to determine valuesfor features until there are no more undetermined features. In order todetermine 150 values for each subsequent undetermined feature in thesynthetic data case, the already-determined feature values are used todetermine 120 the K nearest neighbors (a new set of focal cases) to thatset of already-determined values (e.g., all of the feature values set tothat point). For example, if values A=3, B=5, and C=9.7 have alreadybeen set for the synthetic data case, either via conditioning or usingprocess 100 (and value D is next to be determined), then the K nearestneighbors to the values for A, B, and C will be the new set of focalcases. Then the distribution (e.g., DistD) for that subsequentundetermined feature (e.g., feature D) is determined 140 for the new setof focal cases. A value for the subsequent undetermined feature (e.g.,D) is the determined based on a random sampling of the distribution(e.g., DistD) determined for that feature. When all of the featurevalues have been determined 150, then the synthetic data case iscomplete.

In some embodiments, determining 150 a value for a nominal feature mayinclude skipping the distribution determining 140 step, and insteaddetermining the value for the feature differently based on the desiredsurprisal or conviction. For example, if the desired conviction isinfinity or an arbitrarily large number (low surprisal), then the valuefor the nominal feature may be chosen so that the data case as a wholerepresents a data case in the original training data cases, thusrepresenting unsurprising results. If conviction is closer to one (e.g.,within a threshold value of one), then the distribution used todetermine the nominal value may be a blend of the global residual (e.g.,the probability of each nominal value in the set of original trainingdata cases) and the local residual (e.g., the probability of eachnominal value in the set of focal cases). Blending the local residualand global residual may take any appropriate form, such as weightingeach of the local and global residual, and combined the two weightedresults. The weights may be any appropriate weight, such as equalweights, or a weighting determined based on conviction. Further, theblending of local and global residual may be more continuous than justtwo samples, involving multiple samples of the distribution based on theconviction. If desired conviction is or approaches zero (highsurprisal), then the value for the nominal may be chosen randomly amongthe possible nominal values (e.g., using a 1/N probability for each of Npossible nominal values). When conviction is outside the thresholds touse the “infinity” conviction, the “one” conviction, and the “zero”conviction, then, in various embodiments, different or similarcalculation methods may be used. For example, in some embodiments, ifdesired conviction is less than one half, then the calculationtechniques associated with a conviction of zero may be used. In someembodiments, a nominal value can be chosen based on a weighting of thetwo techniques, optionally with the distance to the key conviction valuebeing the weighting. For example, with a desired conviction of 0.5, theweighting between use of 1/N probability for each of N possible nominalvalues may be weighted 50% and the blending of global and localresiduals may be weighted 50% and the value for the feature may bedetermined based on the weighted results.

In some embodiments, optionally, the synthetic data case can be testedfor fitness 160. Testing the synthetic data case for fitness 160 caninclude any appropriate technique, including confirming that thesynthetic data case meets any received 110 conditions, or whether itmeets other criteria, such as a fitness score or function. The fitnessscore or function may be any appropriate function. In some embodiments,the fitness function depends on the domain of the synthetic data caseand can be a measure of performance of the synthetic data case ascompared to other data cases. For example, the fitness function may be ameasure of speed, processing efficiency, or some other measure ofperformance. Further, the fitness function might be modified at random,to introduce additional variation. Further, as discussed herein,determining the fitness of the synthetic data case may includedetermining the k-anonymity, validity, and/or similarity of thesynthetic data case.

In some embodiments, after the completion of determination and testingof the synthetic data case, optionally, more synthetic data may begenerated, as indicated by the dashed line from 170 to 120 (in the caseof FIG. 1A and FIG. 1B) and/or 121 (in the case of FIG. 1C). Thedecision to generate more synthetic data may be based on the received110 request for synthetic data. For example, the received 110 requestmay indicate that a certain number (or at least a certain number) ofsynthetic data cases are to be generated, that cases are generated basedon a threshold for surprisal or conviction for new cases (e.g., generatecases as long as the surprisal of each new case is at least beyond acertain threshold), and/or the like. The process will then proceed asdiscussed herein. In some embodiments, not depicted in FIG. 1A, FIG. 1B,or FIG. 1C, the process will return to receive 110 more requests forsynthetic data before proceeding to determine and test more syntheticdata case(s).

Upon completion of determination and testing of the synthetic data case,optionally, it can be provided 170 as synthetic data. For example, thesynthetic data case may be provided 170 in response to the received 110request for data. In some embodiments, multiple synthetic data cases maybe created in response to receiving 110 the original request, and may beprovided 170 in response to that request. Providing the synthetic datacase(s) in response to the request can take any appropriate form,including having them sent via HTTP, HTTPS, FTP, FTPS, via an API, aremote procedure call, a function or procedure call, etc., and/or inresponse to one of the foregoing.

In some embodiments, optionally, after one or more synthetic data caseshave been created, control of a controllable system can be caused 199based at least in part on the synthetic data case(s) created usingprocess 100. For example, not depicted in FIG. 1A, a computer-basedreasoning model may be trained based on the synthetic data case(s)(and/or other sets of synthetic data cases, the training cases, and or acombination of such cases, or a combination of (sub)sets of such cases,etc.), and that model may be used to control a controllable system.Numerous examples of causing 199 control of a controllable system arediscussed herein and include, manufacturing control, vehicle control,image labelling control, smart device control, federated system control,etc.

Additional Example Processes for Synthetic Data Generation: ValueChecking

FIG. 1B is a flow diagram depicting example processes for synthetic datageneration in computer-based reasoning systems. Similar numbers and textare used to describe similar actions in FIG. 1A, FIG. 1B, and FIG. 1C.The process 100 described in FIG. 1B may include different and/oradditional steps. For example, turning to FIG. 1B, as indicated by thedotted line, after the value for a feature is determined 150, thetechniques optionally include checking whether the determined 150 valueis valid 152. In some embodiments, the determined 150 value is checkedfor validity 152 using feature information, such as feature boundsand/or k-anonymity. For example, if feature bounds are known for aspecific feature, and the value determined 150 for that feature isoutside the feature bounds, then corrective action can be taken. Notpictured in FIG. 1 , feature bounds may be determined based on themodel, the data (e.g., observed and/or determined bounds of thefeature), provided by an operator (e.g., set bounds of the feature),and/or the like. In some embodiments, other feature information may alsobe used to check whether the value is valid 152. For example, if thefeature is related to one or more other features via correlation,inversely, or via other known relationship, then the value for thefeature can be checked for validity based on the values of the one ormore other related features. As a more specific example, if a featureindicates that a distributable agricultural quantity may be fertilizeror seeds, then the distribution rate bounds may differ considerablybased on the type of distributable commodity. Nitrogen may have adistribution rate between one and six pounds per one thousand squarefeet. Ryegrass seeds, on the other hand, may have a distribution ratebetween five and ten pounds per one thousand square feet. Further, eachof these distribution rates may be influenced by geographic region(e.g., as another feature). As such, checking the validity 152 of adistribution rate value may depend on both the type of agricultural itembeing distributed (a first additional feature) as well as geographicregion (a second additional feature).

If the determined 150 value is determined to be invalid 152, thencorrective action can be performed. Corrective action can include one ormore of re-determining a value for the feature (e.g., as described withrespect to determining 150), and optionally again checking the validity152 of the newly determined 150 value. This process may continue until avalid value is determined, and/or up to a certain number of attemptsbefore performing a different type of corrective action. In someembodiments, either after first re-determining 150 a new value for thefeature, or instead of re-determining 150 a new value for the feature, anew value for the feature can be determined using the featureinformation, such as feature range. For example, the new value may bedetermined using a distribution across the feature range (e.g., using auniform distribution, a normal distribution, etc.). In some embodiments,the new value for the feature may instead be determined by capping it atthe feature boundaries.

Additional Example Processes for Synthetic Data Generation: Identityand/or Similarity Checking

As another example of the additions and or changes to the techniques orprocess 100 depicted in FIG. 1B and FIG. 1C (vs. FIG. 1A), thetechniques may include, in addition to or instead of optionallydetermining fitness 160 of a synthetic data case, determining similarity160 of the synthetic data case with all or a portion of the trainingdata. Further, in some embodiments, even if not depicted in the figures,determining the validity 152, fitness 160, and/or similarity 160 ofgenerated data may be performed as part of the same step. As indicatedin FIG. 1A. For example, in FIG. 1A, validity, fitness, and/orsimilarity are all performed as part of determining fitness 160 of thegenerated data. In some embodiments, determining fitness, validity, andsimilarity may be included in a single term of validity, fitness, orsimilarity.

As noted above, in some embodiments, it may be important for synthetictraining data to differ from the existing training data. As such, thegenerated data may be checked for similarity with the training data.This similarity test may be based on a distance measure of all and/or asubset of the features. For example, if a subset of features isconsidered particularly sensitive or important, the distance measure maybe used based on that subset of features. For example, instead of, or inaddition to, checking the distance of all features between the generateddata and the training data, the distance of a subset of the features maybe checked. Furthering the example, if three features have beendetermined or are known to be particularly determinative, then thedistance or similarity of those features for the generated data may bedetermined (as compared to the training data).

Similarity may be determined using Euclidean distance, Minkowskidistance, Damerau-Levenshtein distance, Kullback-Leibler divergence,cosine similarity, Jaccard index, Tanimoto similarity, and/or any otherdistance measure, metric, pseudometric, premetric, index, and/or thelike. In the event that synthetic training data is identical or toosimilar to existing training data (e.g., as measured by one of themeasures discussed above), the synthetic training case may be modified(e.g., resampled) and retested, discarded, and/or replaced.

One or more newly generated synthetic data cases may be tested againstat least a portion of the training data. For example, in someembodiments, it may be important that newly generated cases differ fromthe all data in the training data. As such, newly generated data casesmay be assessed for similarity with all data cases in the training data.If over-similarity is found, then the synthetic case may be modified(and retested), discarded, or replaced (and the replacement syntheticcase tested for similarity). In some embodiments, it may only beimportant that the synthetic data case differ from a portion of theexisting training data. As such, the newly generated data case may betested for similarity to only that portion of the cases. As a morespecific example, if portions of the training data have personalinformation and other portions do not (e.g., the latter may have beensynthetically generated, and/or may be part of the public domain), thenthe test for similarity may only be with respect to the portion ofconcern—the former portion containing personal information. In someembodiments, determining 160 similarity (or fitness) of the generated orsynthetic data case may include determining the k-anonymity of the datacase (as discussed elsewhere herein).

As noted above, determining the similarity of the synthetic trainingcases to the existing training data may be important in embodimentswhere the synthetic data needs to exclude identical and/or merelysimilar synthetic data as compared to the existing training data. Forexample, if there is personally identifiable information and/or datausable to re-identify an individual in the existing training data, thenit may be useful if the synthetic data differs enough from the existingtraining data in order to ensure that the synthetic data cannot bere-identified to persons in the existing training data. As a morespecific example, if the synthetic training case were generated asidentical to a training case in the existing training data for person X,then that synthetic training case would be re-identifiable to person X.In contexts where it is desirable that the synthetic data does notrepresent any person from the existing training data, then it may bebeneficial to determine whether there is any synthetic data that can bere-identified and modify or discard that synthetic data. Further,whether or not the data includes personally identifiable information,ensuring that the synthetic data cases differ from the existing trainingdata may still be desirable. For example, it may be desirable that thesynthetic training data not represent any particular existing trainingdata in order to ensure that a model can be made without including thattraining data. For example, a data owner may want to share data (e.g.,data related to machine vibration analysis) with a collaborator company,but may not want to share data specific to their operation. As such,they may desire that the synthetic training data not be overly similarto their existing training data, but still be useful for thecollaborator company. The same can be true for a portion of the data.For example, the data owner may want to ensure that data related to aparticular driver (for self-driving vehicles) not be included in thesynthetic training data. As such, that driver's data may be checkedagainst the synthetic training data for over similarity, and anyoverly-similar data may be modified or discarded.

Additional Example Processes for Synthetic Data Generation

FIG. 1C is a flow diagram depicting example processes for synthetic datageneration in computer-based reasoning systems. Similar numbers and textare used to describe similar actions in FIG. 1A, FIG. 1B, and FIG. 1C.The process 100 described in FIG. 1C may include different and/oradditional steps. For example, turning to FIG. 1C, the process depictedin FIG. 1C includes determining 121 one or more initial case(s). In someembodiments, there may be one initial case, and this initial case may beused as the basis on which the synthetic data is generated. The initialcase may be chosen randomly from among the cases in the training data;the values of the features for the initial case may be chosen at random(e.g., set as random values that satisfy the type (e.g., integer,positive number, nominal, etc.) of the feature, such as choosing aninteger if the feature requires an integer); may be chosen at randombased on the distribution of the features in the training data (asdiscussed elsewhere herein); may be chosen to match one or more (or all)features of a case in the training data; a combination of these, etc. Insome embodiments, two or more seed cases may be chosen (e.g., based onone or more conditions, at random, etc.), and the initial case can bedetermined based on the two or more seed cases (e.g., averaging thevalues from among the cases for each feature, using a voting mechanismfor feature values, choosing randomly among the feature values of theseed cases, and/or the like).

After the initial case is determined 121, a feature in the case isselected 130 to be replaced, which may also be termed “dropping” thefeature from the case. Selecting a feature is described elsewhereherein, and can include selecting a feature at random, selecting afeature with the lowest or highest conviction (of any type), and or anyother technique, including those discussed herein. Once a feature isselected 130, then a value for the feature may be determined 150.Determining 150 the value for the feature may be accomplished based onthe determined 140 distribution for the feature in the training data (asdescribed elsewhere herein) as a whole (sometimes referred to as theglobal model). In some embodiments, the value for the features may bedetermined 150 by determining 140 the distribution for the feature forthe local model (e.g., the k nearest neighbors). For example, in someembodiments, the closest k nearest neighbors of the case with thefeature dropped out may be determined and the value for the feature maybe determined based on the corresponding values for the feature in the knearest neighbors. The local model distribution may be determined 140and used to determine 150 the value for the feature. In someembodiments, not depicted in FIG. 1C, the value of the feature for thesynthetic case can be determined from the corresponding values from thek nearest neighbors in any appropriate manner (e.g., not based on thelocal model distribution), including averaging the values from among thecases for each feature, using a voting mechanism among the values fromamong the cases for each feature, choosing randomly among the values,and/or the like.

In some embodiments, the value for more than one feature may be droppedout and determined 140 and/or 150 at the same time. For example, two,three, seven, etc. features may be dropped out, and valued for thosetwo, three, seven, etc. features may be determined based on a globalmodel, a local model, etc. as described herein.

In some embodiments, features are dropped out, and new values aredetermined 140 and/or 150 iteratively until a termination condition ismet, as indicated by the dashed line between determining 150 andselecting 130. For example, in some embodiments, features will bedropped out and redetermined a fixed number of times (e.g., if there areF features, then this might happen a multiple of F times (e.g., 6*F), ora fixed number of times, such as 10, 100, 2000, 10{circumflex over( )}7, etc., regardless of F). Further, features may be dropped out andadded back in in an order that ensures that each feature is replaced thesame number of times, or a similar number of times (e.g., continue untilall features have been replaced six times or replace all features untileach feature has been replaced at least six times, but no more thanseven times). In some embodiments, the number of times that a feature isreplaced is not measured against the number of times that other featuresare dropped out and replaced, and the techniques proceed irrespective ofthat measure.

In some embodiments, features are dropped out and added back in untilthere is below a threshold difference between the case before and afterthe feature was dropped out and the value for it redetermined. Forexample, the distance between the case before the feature(s) are droppedout and replaced and the case after feature value(s) are replaced can bedetermined. In some embodiments, if the distance is below a particularthreshold, then the process of iteratively dropping and replacing valueswill terminate. In some embodiments, the distance of the last Rreplacements may be measured. For example, if the distance between thepre-replacement case and the post-replacement case for the last Rreplacements is below a particular threshold, then the dropping andreplacing of values will terminate. The distance measure used may be anyappropriate distance measure, including those discussed herein andEuclidean distance, Minkowski distance, Damerau-Levenshtein distance,Kullback-Leibler divergence, cosine similarity, Jaccard index, Tanimotosimilarity, and/or any other distance measure, metric, pseudometric,premetric, index, or the like.

In some embodiments, the dropping and replacement of feature values maycontinue until the case is within a threshold distance of a case in thetraining data. For example, if a case is below a certain thresholddistance to a case in the training data, the dropping and replacement offeature values may be terminated. This may be beneficial when it is thecase that having a close distance between the synthetic case and a casein the training data means that the synthetic case is sufficientlysimilar to cases in the training data. Further, in some embodiments, thedropping and replacement of feature values may only terminate if thedistance is below a certain closeness threshold, but above a second,identity threshold. This approach may be beneficial when being closerthan an identity threshold means that the synthetic case risks beingoverly similar, and possibly identifiable as, a case in the trainingdata.

In some embodiments, features are dropped out and added back in untilthere is below a threshold distance between the value of a featuredropped out and that added back in. For example, if the replacementvalue of a feature is within a certain predefined distance of the valueit is replacing, then the process of dropping and replacing values maystop. In some embodiments, the differences of the values for the last Rreplacements are checked to determine if, together, they are below aparticular threshold. If they are, then the process of dropping andreplacing values may be terminated and process 100 may continue.

In some embodiments, features are dropped out and added back in until acombination of conditions are met. For example, in some embodiments, acheck may be made to ensure that both (or either) of the conditions ofthe case changing by less than a first threshold distance is met andthere having been M iterations already performed.

Example Time Series Embodiments

Data cases are discussed extensively elsewhere herein. Further to thosediscussions, in some embodiments, data cases may include featuresrelated to time series information for the features. For example, a datacase at time “t0” may include N features. In some embodiments, inaddition to those N features from time t0, one or more time seriesfeatures may also be included, such as values from t(−1), t(−2), etc.The time series features may be previous value(s) for feature(s),difference(s) between the current value(s) and previous value(s),interpolated value(s) of previous value(s) for a fixed lag, differencesbetween the current and previous value(s) divided by the timestep ortime delta to the previous value(s) (akin to a “velocity”), and/orhigher derivatives such as dividing by time twice or taking the changein velocity (acceleration). Further, in some embodiments, time (e.g., asa value that indicates when the data case was collected or as a valueindicating the delta to the previous time step) may be included includeas a feature, and/or unique IDs to each time series of data is included.Some embodiments include feature weighting (discussed elsewhere herein),that allows inclusion of more than one approach to time seriesinclusion, and determination of what works best.

As noted above, the previous values may be included for less than the Ntotal features. For example, it may be known that some features do notor change little change over time (e.g., names of an individual may beassumed to not change over a small time period).

Time series features may be used in the same manner as other features.For example, the time series information for a data case may be used indistance measure(s), may be used to test k-anonymity, similarity,validity, closeness, etc. Further, in some embodiments, time seriesinformation may be generated as part of determining a synthetic datacase.

Reinforcement Learning and Other Additional Embodiments

In some embodiments, the techniques may be used for reinforcementlearning. For example, each time a synthetic training case is created,then the set of training cases can be updated and new synthetic data canbe generated based on the updated set of training cases. In someembodiments, the techniques herein are used for reinforcement learning.For reinforcement learning, the outcome or goal feature(s) (e.g., thescore of a game, or having a winning checkers match) are treated asconditioned inputs or features. For example, in the checkers example,the synthetic data case is generated with conditions of the current gameboard setup and where the move was part of a winning strategy. The“winning strategy” feature may have been set in the training data set.For example, once a game has been won, an outcome feature is set toeither “winning” or “losing” for all moves that had been made in thegame. As such, each move in a winning game has the outcome feature setto “winning” and each move in a losing game has outcome set to “losing.”As such, then the data is conditioned to pick only moves that are partof a winning game, that feature (outcome=“winning”) is used in the KNNcalculation discussed elsewhere herein.

The reinforcement learning scenarios can also include ranges (like ascore above, below, or within a certain threshold), and other criteria.For example, as discussed elsewhere herein, the techniques herein can beuseful in reinforcement learning situations where synthetic data isneeded on expensive, dangerous, and/or hard to reproduce scenarios. Forexample, if pipelines only fail (e.g., leak, explode, become clogged)0.001% of the time, but training data is needed to train acomputer-based reasoning system to detect when those scenarios are goingto happen, the techniques herein can be used to synthesize training datafor those rare cases. This allows additional training data for pipelinefailure to be gathered without incurring the difficulty, danger, andcost of actual pipeline failures. In such an example, the failure of thepipeline could be one of the conditions on the synthetic data. So, asdata is being generated, the focal cases determined 120 will be thoseassociated with pipeline failure, and the subsequently generatedfeatures will represent the distribution of values of those featureswithin the conditioned data.

In some embodiments, the techniques may be used to create synthetic datathat replicates users, devices, etc. For example, data that is based on,is similar to user data (or device data, etc.) can be created using thetechniques herein. Consider user data that cannot be used (because it isnot anonymous) and where one would prefer not to anonymize the data.That data can be used to create synthetic user data. If the dataincludes personally identifiable information as features (e.g., name,SSN, etc.), those features could be assigned random values, and the restof the features can be synthesized based on user data (and possiblyconditions) using the techniques discussed herein. Alternatively, insome embodiments, features containing personally identifiableinformation could also be generated based on existing user data, butwith very high surprisal, creating a much wider distribution than seenin the user data.

Overview of Surprisal, Entropy, and Divergence

Below is a brief summary of some concepts discussed herein. It will beappreciated that there are numerous ways to compute the concepts below,and that other, similar mathematical concepts can be used with thetechniques discussed herein.

Entropy (“H(x)”) is a measure of the average expected value ofinformation from an event and is often calculated as the sum overobservations of the probability of each observation multiple by thenegative log of the probability of the observation.H(x)=−Σ_(i) p(x _(i))*log p(x _(i))

Entropy is generally considered a measure of disorder. Therefore, highervalues of entropy represent less regularly ordered information, withrandom noise having high entropy, and lower values of entropy representmore ordered information, with a long sequence of zeros having lowentropy. If log₂ is used, then entropy may be seen as representing thetheoretical lower bound on the number of bits needed to represent theinformation in a set of observations. Entropy can also be seen as howmuch a new observation distorts a combined probability density or massfunction of the observed space. Consider, for example, a universe ofobservations where there is a certain probability that each of A, B, orC occurs, and a probability that something other than A, B, or C occurs.

Surprisal (“I(x)”) is a measure of how much information is provided by anew event x_(i).I(x _(i))=−log p(x _(i))

Surprisal is generally a measure of surprise (or new information)generated by an event. The smaller the probability of X_(i), the higherthe surprisal.

Kullback-Leibler Divergence (“KL divergence” or “Div_(KL)(x)”) is ameasure of difference in information between two sets of observation. Itis often represented asDiv _(KL)(x)=Σ_(i) p(x _(i))*(log p(x _(i))−log q(x _(i))),

where p(x_(i)) is the probability of x_(i) after x_(i) has occurred, andq(x_(i)) is the probability of x_(i) before x_(i) has occurred.

Familiarity Conviction Examples

Conviction and contribution measures may be used with the techniquesherein. In some embodiments, conviction measures may be related invarious ways to surprisal, including conviction being related to theratio of observed surprisal to expected surprisal. Various of theconviction and contribution measures are discussed herein, includingfamiliarity conviction discussed next.

In some embodiments, it may be useful to employ conviction as measure ofhow much information the point distorts the model. To do so, one maydefine a feature information measure, such as familiarity conviction,such that a point's weighted distance contribution affects other points'distance contribution and compared to the expected distance contributionof adding any new point.

Definition 1. Given a point x∈X and the set K of its k nearestneighbors, a distance function d: R^(z)×Z→R, and a distance exponent α,the distance contribution of x may be the harmonic mean

$\begin{matrix}{{\phi(x)} = {\left( {\frac{1}{❘K❘}{\sum\limits_{k \in K}\frac{1}{{d\left( {x,k} \right)}^{\alpha}}}} \right)^{- 1}.}} & (3)\end{matrix}$

Definition 2. Given a set of points X⊂R^(z) for every x∈X and an integer1≤k<|X| one may define the distance contribution probabilitydistribution, C of X to be the set

$\begin{matrix}{C = \left\{ {\frac{\phi\left( x_{1} \right)}{\sum\limits_{i = 1}^{n}{\phi\left( x_{i} \right)}},\frac{\phi\left( x_{2} \right)}{\sum\limits_{i = 1}^{n}{\phi\left( x_{i} \right)}},\ldots,\frac{\phi\left( x_{n} \right)}{\sum\limits_{i = 1}^{n}{\phi\left( x_{i} \right)}}} \right\}} & (4)\end{matrix}$for a function φ: X→R that returns the distance contribution.

Note that if φ(0)=∞, special consideration may be given to multipleidentical points, such as splitting the distance contribution amongthose points.

Remark 1. C may be a valid probability distribution. In someembodiments, this fact is used to compute the amount of information inC.

Definition 3. The point probability of a point x_(i), i=1,2, . . . , nmay be

$\begin{matrix}{{l(i)} = \frac{\phi\left( x_{i} \right)}{\sum\limits_{i}{\phi\left( x_{i} \right)}}} & (5)\end{matrix}$where the index i is assigned the probability of the indexed point'sdistance contribution. One may denote this random variable L.

Remark 2. When points are selected uniformly at random, one may assume Lis uniform when the distance probabilities have no trend or correlation.

Definition 4. The conviction of a point x_(i) ∈X may be

$\begin{matrix}{{\pi_{f}\left( x_{i} \right)} = \frac{\frac{1}{❘X❘}{\sum\limits_{i}{{\mathbb{K}\mathbb{L}}\left( {{{L{❘❘}L} - \left\{ i \right\}}\bigcup{{\mathbb{E}}{l(i)}}} \right)}}}{{\mathbb{K}\mathbb{L}}\left( {{{L{❘❘}L} - \left\{ x_{i} \right\}}\bigcup{{\mathbb{E}}{l(i)}}} \right)}} & (6)\end{matrix}$where KL is the Kullback-Leibler divergence. In some embodiments, whenone assumes L is uniform, one may have that the expected probability

${{\mathbb{E}}{l(i)}} = {\frac{1}{n}.}$

Prediction Conviction Examples

In some embodiments, it is useful to employ conviction as a proxy foraccuracy of a prediction. To do so, one may define another type ofconviction such that a point's weighted distance to other points is ofprimary importance and can be expressed as the information required todescribe the position of the point in question relative to existingpoints.

Definition 5. Let ζ be the number of features in a model and n thenumber of observations. One may define the residual function of thetraining data X:r:X→R ^(ζ)r(x)=J ₁(k,p),J ₂(k,p), . . . ,J _(ζ)(k,p)  (7)

Where J_(i) may be the residual of the model on feature i parameterizedby the hyperparameters k and p evaluated on points near x. In someembodiments, one may refer to the residual function evaluated on all ofthe model data as r_(M). in some embodiments, the feature residuals maybe calculated as mean absolute error or standard deviation.

In some embodiments, one can quantify the information needed to expressa distance contribution φ(x) by moving to a probability. In someembodiments, the exponential distribution may be selected to describethe distribution of residuals, as it may be the maximum entropydistribution constrained by the first moment. In some embodiments, adifferent distribution may be used for the residuals, such as theLaplace, lognormal distribution, Gaussian distribution, normaldistribution, etc.

The exponential distribution may be represented or expressed as:

$\begin{matrix}{\frac{1}{\lambda} = {{r(x)}}_{p}} & (8)\end{matrix}$

We can directly compare the distance contribution and p-normed magnitudeof the residual. This is because the distance contribution is a locallyweighted expected value of the distance from one point to its nearestneighbors, and the residual is an expected distance between a point andthe nearest neighbors that are part of the model. Given the entropymaximizing assumption of the exponential distribution of the distances,we can then determine the probability that a distance contribution isgreater than or equal to the magnitude of the residual ∥r(x)∥_(p) as:

$\begin{matrix}{{P\left( {{\varphi(x)} \geq {{r(x)}}_{p}} \right)} = {e^{{- \frac{1}{{{r(x)}}_{p}}} \cdot {\varphi(x)}}.}} & (9)\end{matrix}$

We then convert the probability to self-information as:I(x)=−ln P(φ(x)≥∥r(x)∥_(p)),  (10)which simplifies to:

$\begin{matrix}{{I(x)} = {\frac{\varphi(x)}{{{r(x)}}_{p}}.}} & (11)\end{matrix}$

As the distance contribution decreases, or as the residual vectormagnitude increases, the less information may be needed to representthis point. One can then compare this to the expected value a regularconviction form, yielding a prediction conviction of:

$\begin{matrix}{{\pi_{p} = \frac{EI}{I(x)}},} & (12)\end{matrix}$where I is the self-information calculated for each point in the model.

Additional Prediction Conviction Examples

In some embodiments, p(x) may be the distance contribution of point x,and r(x) may be the magnitude of the expected feature residuals at pointx using the same norm and same topological parameters as the distancecontribution, putting both on the same scale.

The probability of both being less than the expected values may be:P(ϕ(x)>

ϕ(x))·P(r(x)>

r(x)).

The self-information of this, which may be the negative log of theprobability

$I = {\frac{\phi(x)}{{\mathbb{E}}{\phi(x)}} + {\frac{r(x)}{{\mathbb{E}}{r(x)}}.}}$

The prediction conviction

$\pi_{p} = \frac{{\mathbb{E}}I}{I}$then may be calculated as:which simplifies to

$\pi_{p} = {\frac{2}{\frac{\phi(x)}{{\mathbb{E}}{\phi(x)}} + \frac{r(x)}{{\mathbb{E}}{r(x)}}}.}$

Feature Prediction Contribution Examples

In some embodiments, another feature information measure, FeaturePrediction Contribution, may be related Mean Decrease in Accuracy (MDA).In MDA scores are established for models with all the features M andmodels with each feature held out M−_(f) _(i) _(i), i=1 . . . ζ. Thedifference |M−M_(f) _(i) | is the importance of each feature, where theresult's sign is altered depending on whether the goal is to maximize orminimize score.

In some embodiments, prediction information π_(c) is correlated withaccuracy and thus may be used as a surrogate. The expectedself-information required to express a feature is given by:

${{{EI}(M)} = {\frac{1}{\xi}{\sum\limits_{i}^{\xi}{I\left( x_{i} \right)}}}},$and the expected self-information to express a feature without feature iis

${{EI}\left( M_{- i} \right)} = {\frac{1}{\xi}{\sum\limits_{j = 0}^{\xi}{{I_{- i}\left( x_{j} \right)}.}}}$

One can now make two definitions:

Definition 6. The prediction contribution π_(c) of feature i is

${\pi_{c}(i)} = {\frac{M - M_{- f_{i}}}{M}.}$

Definition 7. The prediction conviction, pi_(p′), of feature i is

${\pi_{p}(i)} = {\frac{\frac{1}{\xi}{\sum\limits_{i = 0}^{\xi}M_{- f_{i}}}}{M_{- f_{i}}}.}$

Synthetic Data Generation Examples

In some embodiments, prediction conviction may express how surprising anobservation is. As such, one may, effectively, reverse the math and useconviction to generate a new sample of data for a given amount ofsurprisal. In some embodiments, generally, the techniques may randomlyselect or predict a feature of a case from the training data and thenresample it.

Given that some embodiments include calculating conditioned localresiduals for a part of the model, as discussed elsewhere herein, thetechniques may use this value to parameterize the random numberdistribution to generate a new value for a given feature. In order tounderstand this resampling method, it may be useful to discuss theapproach used by the Mann-Whitney test, a powerful and widely usednonparametric test to determine whether two sets of samples were drawnfrom the same distribution. In the Mann-Whitney test, samples arerandomly checked against one another to see which is greater, and ifboth sets of samples were drawn from the same distribution then theexpectation is that both sets of samples should have an equal chance ofhaving a higher value when randomly chosen samples are compared againsteach other.

In some embodiments, the techniques herein include resampling a point byrandomly choosing whether the new sample is greater or less than theother point and then draw a sample from the distribution using thefeature's residual as the expected value. In some embodiments, using theexponential distribution yields the double-sided exponentialdistribution (also known as the Laplace distribution), though lognormaland other distributions may be used as well.

If a feature is not continuous but rather nominal, then the localresiduals can populate a confusion matrix, and an appropriate sample canbe drawn based on the probabilities for drawing a new sample given theprevious value.

As an example, the techniques may be used to generate a random value offeature i from the model with, for example, no other conditions on it.Because the observations within the model are representative of theobservations made so far, a random instance is chosen from theobservations using the uniform distribution over all observations. Thenthe value for feature i of this observation is resampled via the methodsdiscussed elsewhere herein.

As another example, the techniques may be used to generate feature j ofa data element or case, given that, in that data element or case,features i∈Ξ have corresponding values x_(i). The model labels feature jconditioned by all x_(i) to find some value t. This new value t becomesthe expected value for the resampling process described elsewhereherein, and the local residual (or confusion matrix) becomes theappropriate parameter or parameters for the expected deviation.

In some embodiments, the techniques include filling in the features foran instance by beginning with no feature values (or a subset of all thefeature values) specified as conditions for the data to generate. Theremaining features may be ordered randomly or may be ordered via afeature conviction value (or in any other manner described herein). Whena new value is generated for the current feature, then the processrestarts with the newly-set feature value as an additional condition onthat feature.

Parameterizing Synthetic Data Via Prediction Conviction Examples

As discussed elsewhere, various embodiments use the double-sidedexponential distribution as a maximum entropy distribution of distancein Lp space. One may then be able to derive a closed form solution forhow to scale the exponential distributions based on a predictionconviction value. For example, a value, v, for the prediction convictionmay be expressed as

$\begin{matrix}{v = {{\pi_{p}(x)} = \frac{EI}{I(x)}}} & (13)\end{matrix}$which may be rearranged as

$\begin{matrix}{{I(x)} = {\frac{EI}{v}.}} & (14)\end{matrix}$Substituting in the self-information described elsewhere herein:

$\begin{matrix}{\frac{\varphi(x)}{{{r(x)}}_{p}} = {\frac{EI}{v}.}} & (15)\end{matrix}$In some embodiments, that the units on both sides of Equation 15 match.This may be the case in circumstances where he natural logarithm andexponential in the derivation of Equation 15 cancel out, but leave theresultant in nats. We can rearrange in terms of distance contributionas:

$\begin{matrix}{{\varphi(x)} = {\frac{{{r(x)}}_{p} \cdot {EI}}{v}.}} & (16)\end{matrix}$If we let p=0, which may be desirable for conviction and other aspectsof the similarity measure, then we can rewrite the distance contributionin terms of its parameter λ_(i), with expected mean of

$\frac{1}{\lambda_{i}}.$This becomes

$\begin{matrix}{{\underset{i}{\Pi}{E\left( {1/\lambda_{i}} \right)}} = {\frac{\underset{i}{\Pi}r_{i}{EI}}{v}.}} & (17)\end{matrix}$

In some embodiments, due to the number of ways surprisal may be assignedor calculated across the features, various solutions may exist. However,unless otherwise specified or conditioned, embodiments may includedistributing surprisal uniformly across the features, holding expectedproportionality constant. In some embodiments, the distance contributionmay become the mean absolute error for the exponential distribution,such as:

$\begin{matrix}{{E\left( {1/\lambda_{i}} \right)} = {r_{i}{\frac{EI}{v}.}}} & (18)\end{matrix}$and solving for the λ_(i) parameterize the exponential distributions mayresult in:

$\begin{matrix}{\lambda_{i} = {\frac{v}{r_{i}{EI}}.}} & (19)\end{matrix}$In some embodiments, Equation 19, when combined with the value of thefeature, may become the distribution by which to generate a new randomnumber under the maximum entropy assumption of exponentially distributeddistance from the value.

Reinforcement Learning Examples

In some embodiments, the techniques can generate data with a controlledamount of surprisal, which may be a novel way to characterize theclassic exploration versus exploitation trade off in searching for anoptimal solution to a goal. Traditionally, pairing a means to search,such as Monte Carlo tree search, with a universal function approximator,such as neural networks, may solve difficult reinforcement learningproblems without domain knowledge. Because the data synthesis techniquesdescribed herein utilize the universal function approximator model (kNN)itself, it enables the techniques to be use in a reinforcement learningarchitecture that is similar and tightly coupled, as described herein.

In some embodiments, setting the conviction of the data synthesis to “1”(or any other appropriate value) yields a balance between explorationand exploitation. Because, in some embodiments, the synthetic datageneration techniques described herein can also be conditioned, thetechniques may condition the search on both the current state of thesystem, as it is currently observed, and a set of goal values forfeatures. In some embodiments, as the system is being trained, it can becontinuously updated with the new training data. Once states areevaluated for their ultimate outcome, a new set of features or featurevalues can be added to all of the observations indicating the finalscores or measures of outcomes (as described elsewhere herein, e.g., inrelation to outcome features). Keeping track of which observationsbelong to which training sessions (e.g., games) may be beneficial as aconvenient way to track and update this data. In some embodiments, giventhat the final score or multiple goal metrics may already be in the kNNdatabase, the synthetic data generation may allow querying for new dataconditioned upon having a high score or winning conditions (or any otherappropriate condition), with a specified amount of conviction.

In some embodiments, the techniques herein provide a reinforcementlearning algorithm that can be queried for the relevant training datafor every decision, as described elsewhere herein. The commonality amongthe similar cases, boundary cases, archetypes, etc. can be combined tofind when certain decisions are likely to yield a positive outcome,negative outcome, or a larger amount of surprisal thus improving thequality of the model. In some embodiments, by seeking high surprisalmoves, the system will improve the breadth of its observations.

Targeted and Untargeted Techniques for Determining Conviction and OtherMeasures

In some embodiments, any of the feature information measures, convictionor contribution measures (e.g., surprisal, prediction conviction,familiarity conviction, and/or feature prediction contribution and/orfeature prediction conviction) may be determined using an “untargeted”and/or a “targeted” approach. In the untargeted approach, the measure(e.g., a conviction measure) is determined by holding out the item inquestion and then measuring information gain associated with putting theitem back into the model. Various examples of this are discussed herein.For example, to measure the untargeted conviction of a case (orfeature), the conviction is measured in part based on taking the case(or feature) out of the model, and then measuring the informationassociated with adding the case (or feature) back into the model.

In order to determine a targeted measure, such as surprisal, conviction,or contribution of a data element (e.g., a case or a feature), incontrast to untargeted measures, everything is dropped from the modelexcept the features or cases being analyzed (the “analyzed dataelement(s)”) and the target features or cases (“target dataelement(s)”). Then the measure is calculated by measure the conviction,information gain, contribution, etc. based on how well the analyzed dataelement(s) predict the target data element(s) in the absence of the restof the model.

In each instance that a measure, such as a surprisal, conviction,contribution, etc. measure, is discussed herein, the measure may bedetermined using either a targeted approach or an untargeted approach.For example, when the term “conviction” is used, it may refer totargeted or untargeted prediction conviction, targeted or untargetedfamiliarity conviction, and/or targeted or untargeted feature predictionconviction. Similarly, when surprisal, information, and/or contributionmeasures are discussed without reference to either targeted oruntargeted calculation techniques, then reference may be being made toeither a targeted or untargeted calculation for the measure.

Systems for Synthetic Data Generation in Computer-Based ReasoningSystems

FIG. 2 is a block diagram depicting example systems for synthetic datageneration in computer-based reasoning systems. Numerous devices andsystems are coupled to a network 290. Network 290 can include theinternet, a wide area network, a local area network, a Wi-Fi network,any other network or communication device described herein, and thelike. Further, numerous of the systems and devices connected to 290 mayhave encrypted communication there between, VPNs, and or any otherappropriate communication or security measure. System 200 includes atraining and analysis system 210 coupled to network 290. The trainingand analysis system 210 may be used for collecting data related tosystems 250-258 and creating computer-based reasoning models based onthe training of those systems. Further, training and analysis system 210may perform aspects of process 100 and/or 400 described herein. Controlsystem 220 is also coupled to network 290. A control system 220 maycontrol various of the systems 250-258. For example, a vehicle control221 may control any of the vehicles 250-253, or the like. In someembodiments, there may be one or more network attached storages 230,240. These storages 230, 240 may store training data, computer-basedreasoning models, updated computer-based reasoning models, and the like.In some embodiments, training and analysis system 210 and/or controlsystem 220 may store any needed data including computer-based reasoningmodels locally on the system.

FIG. 2 depicts numerous systems 250-258 that may be controlled by acontrol system 220 or 221. For example, automobile 250, helicopter 251,submarine 252, boat 253, factory equipment 254, construction equipment255, security equipment 256, oil pump 257, or warehouse equipment 258may be controlled by a control system 220 or 221.

Example Processes for Controlling Systems

FIG. 4 depicts an example process 400 for controlling a system. In someembodiments and at a high level, the process 400 proceeds by receivingor receiving 410 a computer-based reasoning model for controlling thesystem. The computer-based reasoning model may be one created usingprocess 100, as one example. In some embodiments, the process 400proceeds by receiving 420 a current context for the system, determining430 an action to take based on the current context and thecomputer-based reasoning model, and causing 440 performance of thedetermined action (e.g., labelling an image, causing a vehicle toperform the turn, lane change, waypoint navigation, etc.). If operationof the system continues 450, then the process returns to receive 420 thecurrent context, and otherwise discontinues 460 control of the system.In some embodiments, causing 199 performance of a selected action mayinclude causing 440 performance of a determined action (or vice-versa).

As discussed herein the various processes 100, 400, etc. may run inparallel, in conjunction, together, or one process may be a subprocessof another. Further, any of the processes may run on the systems orhardware discussed herein. The features and steps of processes 100, 400could be used in combination and/or in different orders.

Self-Driving Vehicles

Returning to the top of the process 400, it begins by receiving 410 acomputer-based reasoning model for controlling the system. Thecomputer-based reasoning model may be received in any appropriatematter. It may be provided via a network 290, placed in a shared oraccessible memory on either the training and analysis system 210 orcontrol system 220, or in accessible storage, such as storage 230 or240.

In some embodiments (not depicted in FIG. 4 ), an operational situationcould be indicated for the system. The operational situation is relatedto context, but may be considered a higher level, and may not change (orchange less frequently) during operation of the system. For example, inthe context of control of a vehicle, the operational situation may beindicated by a passenger or operator of the vehicle, by a configurationfile, a setting, and/or the like. For example, a passenger Alicia mayselect “drive like Alicia” in order to have the vehicle driver like her.As another example, a fleet of helicopters may have a configuration fileset to operate like Bob. In some embodiments, the operational situationmay be detected. For example, the vehicle may detect that it isoperating in a particular location (area, city, region, state, orcountry), time of day, weather condition, etc. and the vehicle may beindicated to drive in a manner appropriate for that operationalsituation.

The operational situation, whether detected, indicated by passenger,etc., may be changed during operation of the vehicle. For example, apassenger may first indicate that she would like the vehicle to drivecautiously (e.g., like Alicia), and then realize that she is runninglater and switch to a faster operation mode (e.g., like Carole). Theoperational situation may also change based on detection. For example,if a vehicle is operating under an operational situation for aparticular portion of road, and detects that it has left that portion ofroad, it may automatically switch to an operational situationappropriate for its location (e.g., for that city), may revert to adefault operation (e.g., a baseline program that operates the vehicle)or operational situation (e.g., the last used). In some embodiments, ifthe vehicle detects that it needs to change operational situations, itmay prompt a passenger or operator to choose a new operationalsituation.

In some embodiments, the computer-based reasoning model is receivedbefore process 400 begins (not depicted in FIG. 4 ), and the processbegins by receiving 420 the current context. For example, thecomputer-based reasoning model may already be loaded into a controller220 and the process 400 begins by receiving 420 the current context forthe system being controlled. In some embodiments, referring to FIG. 2 ,the current context for a system to be controlled (not depicted in FIG.2 ) may be sent to control system 220 and control system 220 may receive420 current context for the system.

Receiving 420 current context may include receiving the context dataneeded for a determination to be made using the computer-based reasoningmodel. For example, turning to the vehicular example, receiving 420 thecurrent context may, in various embodiments, include receivinginformation from sensors on or near the vehicle, determining informationbased on location or other sensor information, accessing data about thevehicle or location, etc. For example, the vehicle may have numeroussensors related to the vehicle and its operation, such as one or more ofeach of the following: speed sensors, tire pressure monitors, fuelgauges, compasses, global positioning systems (GPS), RADARs, LiDARs,cameras, barometers, thermal sensors, accelerometers, strain gauges,noise/sound measurement systems, etc. Current context may also includeinformation determined based on sensor data. For example, the time toimpact with the closest object may be determined based on distancecalculations from RADAR or LiDAR data, and/or may be determined based ondepth-from-stereo information from cameras on the vehicle. Context mayinclude characteristics of the sensors, such as the distance a RADAR orLiDAR is capable of detecting, resolution and focal length of thecameras, etc. Context may include information about the vehicle not froma sensor. For example, the weight of the vehicle, acceleration,deceleration, and turning or maneuverability information may be knownfor the vehicle and may be part of the context information.Additionally, context may include information about the location,including road condition, wind direction and strength, weather,visibility, traffic data, road layout, etc.

Referring back to the example of vehicle control rules for Bob flying ahelicopter, the context data for a later flight of the helicopter usingthe vehicle control rules based on Bob's operation of the helicopter mayinclude fuel remaining, distance that fuel can allow the helicopter totravel, location including elevation, wind speed and direction,visibility, location and type of sensors as well as the sensor data,time to impact with the N closest objects, maneuverability and speedcontrol information, etc. Returning to the stop sign example, whetherusing vehicle control rules based on Alicia or Carole, the context mayinclude LiDAR, RADAR, camera and other sensor data, locationinformation, weight of the vehicle, road condition and weatherinformation, braking information for the vehicle, etc.

The control system then determined 430 an action to take based on thecurrent context and the computer-based reasoning model. For example,turning to the vehicular example, an action to take is determined 430based on the current context and the vehicle control rules for thecurrent operational situation. In some embodiments that use machinelearning, the vehicle control rules may be in the form of a neuralnetwork (as described elsewhere herein), and the context may be fed intothe neural network to determine an action to take. In embodiments usingcase-based reasoning, the set of context-action pairs closest (or mostsimilar) to the current context may be determined. In some embodiments,only the closest context-action pair is determined, and the actionassociated with that context-action pair is the determined 430 action.In some embodiments, multiple context-action pairs are determined 430.For example, the N “closest” context-action pairs may be determined 430,and either as part of the determining 430, or later as part of thecausing 440 performance of the action, choices may be made on the actionto take based on the N closest context-action pairs, where “distance”for between the current context can be measured using any appropriatetechnique, including use of Euclidean distance, Minkowski distance,Damerau-Levenshtein distance, Kullback-Leibler divergence, and/or anyother distance measure, metric, pseudometric, premetric, index, or thelike.

In some embodiments, the actions to be taken may be blended based on theaction of each context-action pair, with invalid (e.g., impossible ordangerous) outcomes being discarded. A choice can also be made among theN context-action pairs chosen based on criteria such as choosing to usethe same or different operator context-action pair from the lastdetermined action. For example, in an embodiment where there arecontext-action pair sets from multiple operators in the vehicle controlrules, the choice of which context-action pair may be based on whether acontext-action pair from the same operator was just chosen (e.g., tomaintain consistency). The choice among the top N context-action pairsmay also be made by choosing at random, mixing portions of the actionstogether, choosing based on a voting mechanism, etc.

Some embodiments include detecting gaps in the training data and/orvehicle control rules and indicating those during operation of thevehicle (for example, via prompt and/or spoken or graphical userinterface) or offline (for example, in a report, on a graphical display,etc.) to indicate what additional training is needed (not depicted inFIG. 4 ). In some embodiments, when the computer-based reasoning systemdoes not find context “close enough” to the current context to make aconfident decision on an action to take, it may indicate this andsuggest that an operator might take manual control of the vehicle, andthat operation of the vehicle may provide additional context and actiondata for the computer-based reasoning system. Additionally, in someembodiments, an operator may indicate to a vehicle that she would liketo take manual control to either override the computer-based reasoningsystem or replace the training data. These two scenarios may differ bywhether the data (for example, context-action pairs) for the operationalscenario are ignored for this time period, or whether they are replaced.

In some embodiments, the operational situation may be chosen based on aconfidence measure indicating confidence in candidate actions to takefrom two (or more) different sets of control rules (not depicted in FIG.4 ). Consider a first operational situation associated with a first setof vehicle control rules (e.g., with significant training from Aliciadriving on highways) and a second operational situation associated witha second set of vehicle control rules (e.g., with significant trainingfrom Carole driving on rural roads). Candidate actions and associatedconfidences may be determined for each of the sets of vehicle controlrules based on the context. The determined 430 action to take may thenbe selected as the action associated with the higher confidence level.For example, when the vehicle is driving on the highway, the actionsfrom the vehicle control rules associated with Alicia may have a higherconfidence, and therefore be chosen. When the vehicle is on rural roads,the actions from the vehicle control rules associated with Carole mayhave higher confidence and therefore be chosen. Relatedly, in someembodiments, a set of vehicle control rules may be hierarchical, andactions to take may be propagated from lower levels in the hierarchy tohigh levels, and the choice among actions to take propagated from thelower levels may be made on confidence associated with each of thosechosen actions. The confidence can be based on any appropriateconfidence calculation including, in some embodiments, determining howmuch “extra information” in the vehicle control rules is associated withthat action in that context.

In some embodiments, there may be a background or baseline operationalprogram that is used when the computer-based reasoning system does nothave sufficient data to make a decision on what action to take (notdepicted in FIG. 4 ). For example, if in a set of vehicle control rules,there is no matching context or there is not a matching context that isclose enough to the current context, then the background program may beused. If none of the training data from Alicia included what to do whencrossing railroad tracks, and railroad tracks are encountered in lateroperation of the vehicle, then the system may fall back on the baselineoperational program to handle the traversal of the railroad tracks. Insome embodiments, the baseline model is a computer-based reasoningsystem, in which case context-action pairs from the baseline model maybe removed when new training data is added. In some embodiments, thebaseline model is an executive driving engine which takes over controlof the vehicle operation when there are no matching contexts in thevehicle control rules (e.g., in the case of a context-based reasoningsystem, there might be no context-action pairs that are sufficiently“close”).

In some embodiments, determining 430 an action to take based on thecontext can include determining whether vehicle maintenance is needed.As described elsewhere herein, the context may include wear and/ortiming related to components of the vehicle, and a message related tomaintenance may be determined based on the wear or timing. The messagemay indicate that maintenance may be needed or recommended (e.g.,because preventative maintenance is often performed in the timing orwear context, because issues have been reported or detected withcomponents in the timing or wear context, etc.). The message may be sentto or displayed for a vehicle operator (such as a fleet managementservice) and/or a passenger. For example, in the context of anautomobile with sixty thousand miles, the message sent to a fleetmaintenance system may include an indication that a timing belt may needto be replaced in order to avoid a P percent chance that the belt willbreak in the next five thousand miles (where the predictive informationmay be based on previously-collected context and action data, asdescribed elsewhere herein). When the automobile reaches ninety thousandmiles and assuming the belt has not been changed, the message mayinclude that the chance that the belt will break has increased to, e.g.,P*4 in the next five thousand miles.

Performance of the determined 430 action is then caused 440. Turning tothe vehicular example, causing 440 performance of the action may includedirect control of the vehicle and/or sending a message to a system,device, or interface that can control the vehicle. The action sent tocontrol the vehicle may also be translated before it is used to controlthe vehicle. For example, the action determined 430 may be to navigateto a particular waypoint. In such an embodiment, causing 440 performanceof the action may include sending the waypoint to a navigation system,and the navigation system may then, in turn, control the vehicle on afiner-grained level. In other embodiments, the determined 430 action maybe to switch lanes, and that instruction may be sent to a control systemthat would enable the car to change the lane as directed. In yet otherembodiments, the action determined 430 may be lower-level (e.g.,accelerate or decelerate, turn 4° to the left, etc.), and causing 440performance of the action may include sending the action to be performedto a control of the vehicle, or controlling the vehicle directly. Insome embodiments, causing 440 performance of the action includes sendingone or more messages for interpretation and/or display. In someembodiments, the causing 440 the action includes indicating the actionto be taken at one or more levels of a control hierarchy for a vehicle.Examples of control hierarchies are given elsewhere herein.

Some embodiments include detecting anomalous actions taken or caused 440to be taken. These anomalous actions may be signaled by an operator orpassenger, or may be detected after operation of the vehicle (e.g., byreviewing log files, external reports, etc.). For example, a passengerof a vehicle may indicate that an undesirable maneuver was made by thevehicle (e.g., turning left from the right lane of a 2-lane road) or logfiles may be reviewed if the vehicle was in an accident. Once theanomaly is detected, the portion of the vehicle control rules (e.g.,context-action pair(s)) related to the anomalous action can bedetermined. If it is determined that the context-action pair(s) areresponsible for the anomalous action, then those context-action pairscan be removed or replaced using the techniques herein.

Referring to the example of the helicopter fleet and the vehicle controlrules associated with Bob, the vehicle control 220 may determine 430what action to take for the helicopter based on the received 420context. The vehicle control 220 may then cause the helicopter toperform the determined action, for example, by sending instructionsrelated to the action to the appropriate controls in the helicopter. Inthe driving example, the vehicle control 220 may determine 430 whataction to take based on the context of vehicle. The vehicle control maythen cause 440 performance of the determined 430 action by theautomobile by sending instructions to control elements on the vehicle.

If there are more 450 contexts for which to determine actions for theoperation of the system, then the process 400 returns to receive 410more current contexts. Otherwise, process 400 ceases 460 control of thesystem. Turning to the vehicular example, as long as there is acontinuation of operation of the vehicle using the vehicle controlrules, the process 400 returns to receive 420 the subsequent currentcontext for the vehicle. If the operational situation changes (e.g., theautomobile is no longer on the stretch of road associated with theoperational situation, a passenger indicates a new operationalsituation, etc.), then the process returns to determine the newoperational situation. If the vehicle is no longer operating undervehicle control rules (e.g., it arrived at its destination, a passengertook over manual control, etc.), then the process 400 will discontinue460 autonomous control of the vehicle.

Many of the examples discussed herein for vehicles discuss self-drivingautomobiles. As depicted in FIG. 2 , numerous types of vehicles can becontrolled. For example, a helicopter 251 or drone, a submarine 252, orboat or freight ship 253, or any other type of vehicle such as plane ordrone (not depicted in FIG. 2 ), construction equipment, (not depictedin FIG. 2 ), and/or the like. In each case, the computer-based reasoningmodel may differ, including using different features, using differenttechniques described herein, etc. Further, the context of each type ofvehicle may differ. Flying vehicles may need context data such asweight, lift, drag, fuel remaining, distance remaining given fuel,windspeed, visibility, etc. Floating vehicles, such as boats, freightvessels, submarines, and the like may have context data such asbuoyancy, drag, propulsion capabilities, speed of currents, a measure ofthe choppiness of the water, fuel remaining, distance capabilityremaining given fuel, and the like. Manufacturing and other equipmentmay have as context width of area traversing, turn radius of thevehicle, speed capabilities, towing/lifting capabilities, and the like.

Image Labelling

The techniques herein may also be used for image-labeling systems. Forexample, numerous experts may label images (e.g., identifying featuresof or elements within those images). For example, the human experts mayidentify cancerous masses on x-rays. Having these experts label allinput images is incredibly time consuming to do on an ongoing basis, inaddition to being expensive (paying the experts). The techniques hereinmay be used to train an image-labeling computer-based reasoning modelbased on previously-trained images. Once the image-labelingcomputer-based reasoning system has been built, then input images may beanalyzed using the image-based reasoning system. In order to build theimage-labeling computer-based reasoning system, images may be labeled byexperts and used as training data. Using the techniques herein, thesurprisal and/or conviction of the training data can be used to build animage-labeling computer-based reasoning system that balances the size ofthe computer-based reasoning model with the information that eachadditional image (or set of images) with associated labels provides.Once the image-labelling computer-based reasoning is trained, it can beused to label images in the future. For example, a new image may comein, the image-labelling computer-based reasoning may determine one ormore labels for the image, and then the one or more labels may then beapplied to the image. Thus, these images can be labeled automatically,saving the time and expense related to having experts label the images.

In some embodiments, processes 100, 400 may include determining thesurprisal and/or conviction of each image (or multiple images) and theassociated labels or of the aspects of the computer-based reasoningmodel. The surprisal and/or conviction for the one or more images may bedetermined and a determination may be made whether to select or includethe one or more images (or aspects) in the image-labeling computer-basedreasoning model based on the determined surprisal and/or conviction.While there are more sets of one or more images with labels to assess,the process may return to determine whether more image or label setsshould be included or whether aspects should be included and/or changedin the model. Once there are no more images or aspects to consider, theprocess can turn to controlling the image analysis system using theimage-labeling computer-based reasoning.

In some embodiments, process 100 may determine (e.g., in response to arequest) synthetic data for use in the image-labeling computer-basedreasoning model. Based on a model that uses the synthetic data, theprocess can cause 199 control of an image-labeling system using process400. For example, if the data elements are related to images and labelsapplied to those images, then the image-labeling computer-basedreasoning model trained on that data will apply labels to incomingimages. Process 400 proceeds by receiving 410 an image-labelingcomputer-based reasoning model. The process proceeds by receiving 420 animage for labeling. The image-labeling computer-based reasoning model isthen used to determine 430 labels for the input image. The image is thenlabeled 440. If there are more 450 images to label, then the systemreturns to receive 410 those images and otherwise ceases 460. In suchembodiments, the image-labeling computer-based reasoning model may beused to select labels based on which training image is “closest” (ormost similar) to the incoming image. The label(s) associated with thatimage will then be selected to apply to the incoming image.

Manufacturing and Assembly

The processes 100, 400 may also be used for manufacturing and/orassembly. For example, conviction can be used to identify normalbehavior versus anomalous behavior of such equipment. Using thetechniques herein, a crane (e.g., crane 255 of FIG. 2 ), robot arm, orother actuator is attempting to “grab” something and its surprisal istoo high, it can stop, sound an alarm, shutdown certain areas of thefacility, and/or request for human assistance. Anomalous behavior thatis detected via conviction among sensors and actuators can be used todetect when there is some sort breakdown, unusual wear or mechanical orother malfunction, etc. It can also be used to find damaged equipmentfor repairs or buffing or other improvements for any robots or othermachines that are searching and correcting defects in products orthemselves (e.g., fix_(i)ng a broken wire or smoothing out cuts made tothe ends of a manufactured artifact made via an extrusion process).Conviction can also be used for cranes and other grabbing devices tofind which cargo or items are closest matches to what is needed.Conviction can be used to drastically reduce the amount of time to traina robot to perform a new task for a new product or custom order, becausethe robot will indicate the aspects of the process it does notunderstand and direct training towards those areas and away from thingsit has already learned. Combining this with stopping ongoing actionswhen an anomalous situation is detected would also allow a robot tobegin performing work before it is fully done training, the same waythat a human apprentice may help out someone experienced while theapprentice is learning the job. Conviction can also inform what featuresor inputs to the robot are useful and which are not.

As an additional example in the manufacturing or assembly context,vibration data can be used to diagnose (or predict) issues withequipment. In some embodiments, the training data for the computer-basedreasoning system would be vibration data (e.g., the output of one ormore piezo vibration sensors attached to one or more pieces ofmanufacturing equipment) for a piece of equipment along with diagnosisof an issue or error that occurred with the equipment. The training datamay similarly include vibration data for the manufacturing equipmentthat is not associated with an issue or error with the equipment. Insubsequent operation of the same or similar equipment, the vibrationdata can be collected, and the computer-based reasoning model can beused to assess that vibration data to either diagnose or predictpotential issues or errors with the equipment. For example, thevibration data for current (or recent) operation of one or more piecesof equipment, the computer-based reasoning model may be used to predict,diagnose, or otherwise determine issues or errors with the equipment. Asa more specific example, a current context of vibration data for one ormore pieces of manufacturing equipment may result in a diagnosis orprediction of various conditions, including, but not limited to:looseness of a piece of equipment (e.g., a loose screw), an imbalance ona rotating element (e.g., grime collected on a rotating wheel),misalignment or shaft runout (e.g., machine shafts may be out ofalignment or not parallel), wear (e.g., ball or roller bearings, drivebelts or gears become worn, they might cause vibration). As a furtherexample, misalignment can be caused during assembly or develop overtime, due to thermal expansion, components shifting or improperreassembly after maintenance. When a roller or ball bearing becomespitted, for instance, the rollers or ball bearing will cause a vibrationeach time there is contact at the damaged area. A gear tooth that isheavily chipped or worn, or a drive belt that is breaking down, can alsoproduce vibration. Diagnosis or prediction of the issue or error can bemade based on the current or recent vibration data, and a computer-basedreasoning model training data from the previous vibration data andassociated issues or errors. Diagnosing or predicting the issues ofvibration can be especially important where the vibration can causeother issues. For example, wear on a bearing may cause a vibration thatthen loosens another piece of equipment, which then can cause otherissues and damage to equipment, failure of equipment, and even failureof the assembly or manufacturing process.

In some embodiments, techniques herein may determine (e.g., in responseto a request) the surprisal and/or conviction of one or more dataelements (e.g., of the manufacturing equipment) or aspects (e.g.,features of context-action pairs or aspects of the model) to potentiallyinclude in the manufacturing control computer-based reasoning model. Thesurprisal and/or conviction for the one or more manufacturing elementsmay be determined and a determination may be made whether to select orinclude the one or more manufacturing data elements or aspects in themanufacturing control computer-based reasoning model based on thedetermined surprisal and/or conviction. While there are more sets of oneor more manufacturing data elements or aspects to assess (e.g., fromadditional equipment and/or from subsequent time periods), the processmay return to determine whether more manufacturing data elements oraspects sets should be included in the computer-based reasoning model.Once there are no more manufacturing data elements or aspects toconsider for inclusion, the process can turn to controlling themanufacturing system using the manufacturing control computer-basedreasoning system.

In some embodiments, process 100 may determine (e.g., in response to arequest) synthetic data for use in the manufacturing controlcomputer-based reasoning model. Based on a model using the syntheticdata, causing 199 control of a manufacturing system may be accomplishedby process 400. For example, if the data elements are related tomanufacturing data elements or aspects, then the manufacturing controlcomputer-based reasoning model trained on that data will cause controlmanufacturing or assemble. Process 400 proceeds by receiving 410 amanufacturing control computer-based reasoning model. The processproceeds by receiving 420 a context. The manufacturing controlcomputer-based reasoning model is then used to determine 430 an actionto take. The action is then performed by the control system (e.g.,caused by the manufacturing control computer-based reasoning system). Ifthere are more 450 contexts to consider, then the system returns toreceive 410 those contexts and otherwise ceases 460. In suchembodiments, the manufacturing control computer-based reasoning modelmay be used to control a manufacturing system. The chosen actions arethen performed by a control system.

Smart Voice Control

The processes 100, 400 may be used for smart voice control. For example,combining multiple inputs and forms of analysis, the techniques hereincan recognize if there is something unusual about a voice controlrequest. For example, if a request is to purchase a high-priced item orunlock a door, but the calendar and synchronized devices indicate thatthe family is out of town, it could send a request to the person's phonebefore confirming the order or action; it could be that an intruder hasrecorded someone's voice in the family or has used artificialintelligence software to create a message and has broken in. It candetect other anomalies for security or for devices activating at unusualtimes, possibly indicating some mechanical failure, electronics failure,or someone in the house using things abnormally (e.g., a childfrequently leaving the refrigerator door open for long durations).Combined with other natural language processing techniques beyondsentiment analysis, such as vocal distress, a smart voice device canrecognize that something is different and ask, improving the person'sexperience and improving the seamlessness of the device into theperson's life, perhaps playing music, adjusting lighting, or HVAC, orother controls. The level of confidence provided by conviction can alsobe used to train a smart voice device more quickly as it can askquestions about aspects of its use that it has the least knowledgeabout. For example: “I noticed usually at night, but also some days, youturn the temperature down in what situations should I turn thetemperature down? What other inputs (features) should I consider?”

Using the techniques herein, a smart voice device may also be able tolearn things it otherwise may not be able to. For example, if the smartvoice device is looking for common patterns in any of the aforementionedactions or purchases and the conviction drops below a certain threshold,it can ask the person if it should take on a particular action oradditional autonomy without prompting, such as “It looks like you'renormally changing the thermostat to colder on days when you have yourexercise class, but not on days when it is cancelled; should I do thisfrom now on and prepare the temperature to your liking?”

In some embodiments, processes 100, 400 may include determining (e.g.,in response to a request) the surprisal and/or conviction of one or moredata elements (e.g., of the smart voice system) or aspects (e.g.,features of the data or parameters of the model) to potentially includein the smart voice system control computer-based reasoning model. Thesurprisal for the one or more smart voice system data elements oraspects may be determined and a determination may be made whether toinclude the one or more smart voice system data elements or aspects inthe smart voice system control computer-based reasoning model based onthe determined surprisal and/or conviction. While there are more sets ofone or more smart voice system data elements or aspects to assess, theprocess may return to determine whether more smart voice system dataelements or aspects sets should be included. Once there are no moresmart voice system data elements or aspects to consider, the process canturn to controlling the smart voice system using the smart voice systemcontrol computer-based reasoning model.

In some embodiments, process 100 may determine (e.g., in response to arequest) synthetic data for use in the smart voice computer-basedreasoning model. Based on a model that uses the synthetic data, theprocess can cause 199 control of a smart voice system using process 400.For example, if the data elements are related to smart voice systemactions, then the smart voice system control computer-based reasoningmodel trained on that data will control smart voice systems. Process 400proceeds by receiving 410 a smart voice computer-based reasoning model.The process proceeds by receiving 420 a context. The smart voicecomputer-based reasoning model is then used to determine 430 an actionto take. The action is then performed by the control system (e.g.,caused by the smart voice computer-based reasoning system). If there aremore 450 contexts to consider, then the system returns to receive 410those contexts and otherwise ceases 460. In such embodiments, the smartvoice computer-based reasoning model may be used to control a smartvoice system. The chosen actions are then performed by a control system.

Control of Federarted Devices

The processes 100, 400 may also be used for federated device systems.For example, combining multiple inputs and forms of analysis, thetechniques herein can recognize if there is something that shouldtrigger action based on the state of the federated devices. For example,if the training data includes actions normally taken and/or statuses offederated devices, then an action to take could be an often-taken actionin the certain (or related contexts). For example, in the context of asmart home with interconnected heating, cooling, appliances, lights,locks, etc., the training data could be what a particular user does atcertain times of day and/or in particular sequences. For example, if, ina house, the lights in the kitchen are normally turned off after thestove has been off for over an hour and the dishwasher has been started,then when that context again occurs, but the kitchen light has not beenturned off, the computer-based reasoning system may cause an action tobe taken in the smart home federated systems, such as prompting (e.g.,audio) whether the user of the system would like the kitchen lights tobe turned off. As another example, training data may indicate that auser sets the house alarm and locks the door upon leaving the house(e.g., as detected via geofence). If the user leaves the geofencedlocation of the house and has not yet locked the door and/or set thealarm, the computer-based reasoning system may cause performance of anaction such as inquiring whether it should lock the door and/or set analarm. As yet another example, in the security context, the control maybe for turning on/off cameras, or enact other security measures, such assounding alarms, locking doors, or even releasing drones and the like.Training data may include previous logs and sensor data, door or windowalarm data, time of day, security footage, etc. and when securitymeasure were (or should have been) taken. For example, a context such asparticular window alarm data for a particular basement window coupledwith other data may be associated with an action of sounding an alarm,and when a context occurs related to that context, an alarm may besounded.

In some embodiments, processes 100, 400 may include determining thesurprisal and/or conviction of one or more data elements or aspects ofthe federated device control system for potential inclusion in thefederated device control computer-based reasoning model. The surprisalfor the one or more federated device control system data elements may bedetermined and a determination may be made whether to select or includethe one or more federated device control system data elements in thefederated device control computer-based reasoning model based on thedetermined surprisal and/or conviction. While there are more sets of oneor more federated device control system data elements or aspects toassess, the process may return to determine whether more federateddevice control system data elements or aspect sets should be included.Once there are no more federated device control system data elements oraspects to consider, the process can turn to controlling the federateddevice control system using the federated device control computer-basedreasoning model.

In some embodiments, process 100 may determine (e.g., in response to arequest) synthetic data for use in the federated device computer-basedreasoning model. Based on a model that uses the synthetic data, theprocess can cause 199 control of a federated device system using process400. For example, if the data elements are related to federated devicesystem actions, then the federated device control computer-basedreasoning model trained on that data will control federated devicecontrol system. Process 400 proceeds by receiving 410 a federated devicecontrol computer-based reasoning model. The process proceeds byreceiving 420 a context. The federated device control computer-basedreasoning model is then used to determine 430 an action to take. Theaction is then performed by the control system (e.g., caused by thefederated device control computer-based reasoning system). If there aremore 450 contexts to consider, then the system returns to receive 410those contexts and otherwise ceases 460. In such embodiments, thefederated device control computer-based reasoning model may be used tocontrol federated devices. The chosen actions are then performed by acontrol system.

Control and Automation of Experiments

The processes 100, 400 may also be used to control laboratoryexperiments. For example, many lab experiments today, especially in thebiological and life sciences, but also in agriculture, pharmaceuticals,materials science and other fields, yield combinatorial increases, interms of numbers, of possibilities and results. The fields of design ofexperiment, as well as many combinatorial search and explorationtechniques are currently combined with statistical analysis. However,conviction-based techniques such as those herein can be used to guide asearch for knowledge, especially if combined with utility or fitnessfunctions. Automated lab experiments (including pharmaceuticals,biological and life sciences, material science, etc.) may have actuatorsand may put different chemicals, samples, or parts in differentcombinations and put them under different circumstances. Usingconviction to guide the machines enables them to home in on learning howthe system under study responds to different scenarios, and, forexample, searching areas of greatest uncertainty (e.g., the areas withlow conviction as discussed herein). Conceptually speaking, when theconviction or surprisal is combined with a fitness, utility, or valuefunction, especially in a multiplicative fashion, then the combinationis a powerful information theoretic approach to the classic explorationvs exploitation trade-offs that are made in search processes fromartificial intelligence to science to engineering. Additionally, such asystem can automate experiments where it can predict the most effectiveapproach, homing in on the best possible, predictable outcomes for aspecific knowledge base. Further, like in the other embodimentsdiscussed herein, it could indicate (e.g., raise alarms) to humanoperators when the results are anomalous, or even tell which featuresbeing measured are most useful (so that they can be appropriatelymeasured) or when measurements are not sufficient to characterize theoutcomes. This is discussed extensively elsewhere herein. If the systemhas multiple kinds of sensors that have “costs” (e.g., monetary, time,computation, etc.) or cannot be all activated simultaneously, thefeature entropies or convictions could be used to activate or deactivatethe sensors to reduce costs or improve the distinguishability of theexperimental results.

In the context of agriculture, growers may experiment with varioustreatments (plant species or varietals, crop types, seed plantingdensities, seed spacings, fertilizer types and densities, etc.) in orderto improve yield and/or reduce cost. In comparing the effects ofdifferent practices (treatments), experimenters or growers need to knowif the effects observed in the crop or in the field are simply a productof the natural variation that occurs in every ecological system, orwhether those changes are truly a result of the new treatments. In orderto ameliorate the confusion caused by overlapping crop, treatment, andfield effects, different design types can be used (e.g., demonstrationstrip, replication control or measurement, randomized block, split plot,factorial design, etc.). Regardless, however, of the type of test designtype used, determination of what treatment(s) to use is crucial tosuccess. Using the techniques herein to guide treatment selection (andpossible design type) enables experimenters and growers to home in onhow the system under study responds to different treatments andtreatment types, and, for example, searching areas of greatestuncertainty in the “treatment space” (e.g., what are the types oftreatments about which little is known?). Conceptually, the combinationof conviction or surprisal with a value, utility, or fitness functionsuch as yield, cost, or a function of yield and cost, become a powerfulinformation theoretic approach to the classic exploration vsexploitation trade-offs that are made in search processes fromartificial intelligence to science to engineering. Growers can use thisinformation to choose treatments balancing exploitation (e.g., doingthings similar to what has produced high yields previously) andexploration (e.g., trying treatments unlike previous ones, withyet-unknown results). Additionally, the techniques can automateexperiments on treatments (either in selection of treatments, designs,or robotic or automated planting using the techniques described herein)where it can predict the most effective approach, and automaticallyperform the planting or other distribution (e.g., of fertilizer, seed,etc.) required of to perform the treatment. Further, like in the otherembodiments discussed herein, it could indicate (e.g., raise alarms) tohuman operators when the results are anomalous, or even tell whichfeatures being measured are most useful or when measurements are notuseful to characterize the outcomes (e.g., and may possibly be discardedor no longer measured). If the system has types of sensors (e.g., soilmoisture, nitrogen levels, sun exposure) that have “costs” (e.g.,monetary, time, computation, etc.) or cannot be all collected oractivated simultaneously, the feature entropies or convictions could beused to activate or deactivate the sensors to reduce costs whileprotecting the usefulness of the experimental results.

In some embodiments, processes 100, 400 may include determining (e.g.,in response to a request) the surprisal and/or conviction of one or moredata elements or aspects of the experiment control system. The surprisalfor the one or more experiment control system data elements or aspectsmay be determined and a determination may be made whether to select orinclude the one or more experiment control system data elements oraspects in an experiment control computer-based reasoning model based onthe determined surprisal and/or conviction. While there are more sets ofone or more experiment control system data elements or aspects toassess, the process may return to determine whether more experimentcontrol system data elements or aspects sets should be included. Oncethere are no more experiment control system data elements or aspects toconsider, the process can cause 199 control of the experiment controlsystem using the experiment control computer-based reasoning model.

In some embodiments, process 100 may determine (e.g., in response to arequest) synthetic data for use in the experiment control computer-basedreasoning model. Based on a model that uses the synthetic data, theprocess can cause 199 control of an experiment control system usingprocess 400. For example, if the data elements are related to experimentcontrol system actions, then the experiment control computer-basedreasoning model trained on that data will control experiment controlsystem. Process 400 proceeds by receiving 410 an experiment controlcomputer-based reasoning model. The process proceeds by receiving 420 acontext. The experiment control computer-based reasoning model is thenused to determine 430 an action to take. The action is then performed bythe control system (e.g., caused by the experiment controlcomputer-based reasoning system). If there are more 450 contexts toconsider, then the system returns to receive 410 those contexts andotherwise ceases 460. In such embodiments, the experiment controlcomputer-based reasoning model may be used to control experiment. Thechosen actions are then performed by a control system.

Control of Energy Transfer Systems

The processes 100, 400 may also be used for control systems for energytransfer. For example, a building may have numerous energy sources,including solar, wind, grid-based electrical, batteries, on-sitegeneration (e.g., by diesel or gas), etc. and may have many operationsit can perform, including manufacturing, computation, temperaturecontrol, etc. The techniques herein may be used to control when certaintypes of energy are used and when certain energy consuming processes areengaged. For example, on sunny days, roof-mounted solar cells mayprovide enough low-cost power that grid-based electrical power isdiscontinued during a particular time period while costly manufacturingprocesses are engaged. On windy, rainy days, the overhead of runningsolar panels may overshadow the energy provided, but power purchasedfrom a wind-generation farm may be cheap, and only essential energyconsuming manufacturing processes and maintenance processes areperformed.

In some embodiments, processes 100, 400 may include determining (e.g.,in response to a request) the surprisal and/or conviction of one or moredata elements or aspects of the energy transfer system. The surprisalfor the one or more energy transfer system data elements or aspects maybe determined and a determination may be made whether to select orinclude the one or more energy transfer system data elements or aspectsin energy control computer-based reasoning model based on the determinedsurprisal and/or conviction. While there are more sets of one or moreenergy transfer system data elements or aspects to assess, the processmay return to determine whether more energy transfer system dataelements or aspects should be included. Once there are no more energytransfer system data elements or aspects to consider, the process canturn to controlling the energy transfer system using the energy controlcomputer-based reasoning model.

In some embodiments, process 100 may determine (e.g., in response to arequest) synthetic data for use in the energy transfer computer-basedreasoning model. Based on a model that uses the synthetic data, theprocess can cause 199 control of an energy transfer system using process400. For example, if the data elements are related to energy transfersystem actions, then the energy control computer-based reasoning modeltrained on that data will control energy transfer system. Process 400proceeds by receiving 410 an energy control computer-based reasoningmodel. The process proceeds by receiving 420 a context. The energycontrol computer-based reasoning model is then used to determine 430 anaction to take. The action is then performed by the control system(e.g., caused by the energy control computer-based reasoning system). Ifthere are more 450 contexts to consider, then the system returns toreceive 410 those contexts and otherwise ceases 460. In suchembodiments, the energy control computer-based reasoning model may beused to control energy. The chosen actions are then performed by acontrol system.

Health Care Decision Making, Prediction, and Fraud Protection

The processes 100, 400 may also be used for health care decision making,prediction (such as outcome prediction), and fraud detection. Forexample, some health insurers require pre-approval, pre-certification,pre-authorization, and/or reimbursement for certain types of healthcareprocedures, such as healthcare services, administration of drugs,surgery, hospital visits, etc. When analyzing pre-approvals, a healthcare professional must contact the insurer to obtain their approvalprior to administering care, or else the health insurance company maynot cover the procedure. Not all services require pre-approval, but manymay, and which require it can differ among insurers. Health insurancecompanies may make determinations including, but not necessarily limitedto, whether a procedure is medically necessary, whether it isduplicative, whether it follows currently-accepted medical practice,whether there are anomalies in the care or its procedures, whether thereare anomalies or errors with the health care provider or professional,etc.

In some embodiments, a health insurance company may have many “features”of data on which health care pre-approval or reimbursement decisions aredetermined by human operators. These features may include diagnosisinformation, type of health insurance, requesting health careprofessional and facility, frequency and/or last claim of the particulartype, etc. The data on previous decisions can be used to train thecomputer-based reasoning system. The techniques herein may be used toguide the health care decision making process. For example, when thecomputer-based reasoning model determines, with high conviction orconfidence, that a procedure should be pre-approved or reimbursed, itmay pre-approve or reimburse the procedure without further review. Insome embodiments, when the computer-based reasoning model has lowconviction re whether or not to pre-approve a particular procedure, itmay flag it for human review (including, e.g., sending it back to thesubmitting organization for further information). In some embodiments,some or all of the rejections of procedure pre-approval or reimbursementmay be flagged for human review.

Further, in some embodiments, the techniques herein can be used to flagtrends, anomalies, and/or errors. For example, as explained in detailelsewhere herein, the techniques can be used to determine, for example,when there are anomalies for a request for pre-approval, diagnoses,reimbursement requests, etc. with respect to the computer-basedreasoning model trained on prior data. When the anomaly is detected,(e.g., outliers, such as a procedure or prescription has been requestedoutside the normal range of occurrences per time period, for anindividual that is outside the normal range of patients, etc.; and/orwhat may be referred to as “inliers”—or “contextual outliers,” such astoo frequently (or rarely) occurring diagnoses, procedures,prescriptions, etc.), the pre-approval, diagnosis, reimbursementrequest, etc. can be flagged for further review. In some cases, theseanomalies could be errors (e.g., and the health professional or facilitymay be contacted to rectify the error), acceptable anomalies (e.g.,patients that need care outside of the normal bounds), or unacceptableanomalies. Additionally, in some embodiments, the techniques herein canbe used to determine and flag trends (e.g., for an individual patient,set of patients, health department or facility, region, etc.). Thetechniques herein may be useful not only because they can automateand/or flag pre-approval decision, reimbursement requests, diagnosis,etc., but also because the trained computer-based reasoning model maycontain information (e.g., prior decision) from multiple (e.g., 10s,100s, 1000s, or more) prior decision makers. Consideration of this largeamount of information may be untenable for other approaches, such ashuman review.

The techniques herein may also be used to predict adverse outcomes innumerous health care contexts. The computer-based reasoning model may betrained with data from previous adverse events, and perhaps frompatients that did not have adverse events. The trained computer-basedreasoning system can then be used to predict when a current orprospective patient or treatment is likely to cause an adverse event.For example, if a patient arrives at a hospital, the patient'sinformation and condition may be assessed by the computer-basedreasoning model using the techniques herein in order to predict whetheran adverse event is probable (and the conviction of that determination).As a more specific example, if a septuagenarian with a history of lowblood pressure is admitted for monitoring a heart murmur, the techniquesherein may flag that patient for further review. In some embodiments,the determination of a potential adverse outcome may be an indication ofone or more possible adverse events, such as a complication, having anadditional injury, sepsis, increased morbidity, and/or gettingadditionally sick, etc. Returning to the example of the septuagenarianwith a history of low blood pressure, the techniques herein may indicatethat, based on previous data, the possibility of a fall in the hospitalis unduly high (possibly with high conviction). Such information canallow the hospital to try to ameliorate the situation and attempt toprevent the adverse event before it happens.

In some embodiments, the techniques herein include assisting indiagnosis and/or diagnosing patients based on previous diagnosis dataand current patient data. For example, a computer-based reasoning modelmay be trained with previous patient data and related diagnoses usingthe techniques herein. The diagnosis computer-based reasoning model maythen be used in order to suggest one or more possible diagnoses for thecurrent patient. As a more specific example, a septuagenarian maypresent with specific attributes, medical history, family history, etc.This information may be used as the input context to the diagnosiscomputer-based reasoning system, and the diagnosis computer-basedreasoning system may determine one or more possible diagnoses for theseptuagenarian. In some embodiments, those possible diagnoses may thenbe assessed by medical professionals. The techniques herein may be usedto diagnose any condition, including, but not limited to breast cancer,lung cancer, colon cancer, prostate cancer, bone metastases, coronaryartery disease, congenital heart defect, brain pathologies, Alzheimer'sdisease, and/or diabetic retinopathy.

In some embodiments, the techniques herein may be used to generatesynthetic data that mimics, but does not include, previous patient data.This synthetic data generation is available for any of the uses of thetechniques described herein (manufacturing, image labelling,self-driving vehicles, etc.), and can be particularly important incircumstances where using user data (such as patient health data) in amodel may be contrary to policy or regulation. As discussed elsewhereherein, the synthetic data can be generated to directly mimic thecharacteristics of the patient population, or more surprising data canbe generated (e.g., higher surprisal) in order to generate more data inthe edge cases, all without a necessity of including actual patientdata.

In some embodiments, processes 100, 400 may include determining (e.g.,in response to a request) the surprisal and/or conviction of one or moredata elements or aspects of the health care system. The surprisal orconviction for the one or more health care system data elements oraspects may be determined and a determination may be made whether toselect or include the one or more health care system data elements oraspects in a health care system computer-based reasoning model based onthe determined surprisal and/or conviction. While there are more sets ofone or more health care system data elements or aspects to assess, theprocess may return to determine whether more health care system dataelements or aspects should be included. Once there are no more healthcare system data elements or aspects to consider included in the model,the process can turn to controlling the health care computer-basedreasoning system using the health care system computer-based reasoningmodel.

In some embodiments, process 100 may determine (e.g., in response to arequest) synthetic data for use in the health care system computer-basedreasoning model. Based on a model that uses the synthetic data, theprocess can cause 199 control of a health care computer-based reasoningsystem using process 400. For example, if the data elements are relatedto health care system actions, then the health care systemcomputer-based reasoning model trained on that data will control thehealth care system. Process 400 proceeds by receiving 410 a health caresystem computer-based reasoning model. The process proceeds by receiving420 a context. The health care system computer-based reasoning model isthen used to determine 430 an action to take. The action is thenperformed by the control system (e.g., caused by the health care systemcomputer-based reasoning system). If there are more 450 contexts toconsider, then the system returns to receive 410 those contexts andotherwise ceases 460. In such embodiments, the health care systemcomputer-based reasoning model may be used to assess health caredecisions, predict outcomes, etc. In some embodiments, the chosenaction(s) are then performed by a control system.

Real Estate Future Value and Valuation Prediction

The techniques herein may also be used for real estate value estimation.For example, the past values and revenue from real estate ventures maybe used as training data. This data may include, in addition to value(e.g., sale or resale value), compound annual growth rate (“CAGR”),zoning, property type (e.g., multifamily, Office, Retail, Industrial),adjacent business and types, asking rent (e.g., rent per square foot(“sqft”) for each of Office, Retail, Industrial, etc. and/or per unit(for multifamily buildings), further, this may be based on allproperties within the selected property type in a particular geography,for example), capitalization rate (or “cap rate” based on all propertieswithin selected property type in a geography), demand (which may bequantified as occupied stock), market capitalization (e.g., an averagemodeled price per sqft multiplied by inventory sqft of the givenproperty type and/or in a given geography), net absorption (net changein demand for a rolling 12 month period), net completions (e.g., netchange in inventory sqft (Office, Retail, Industrial) or units(Multifamily) for a period of time, such as analyzed data element(s)rolling 12 month period), occupancy (e.g., Occupied sqft/total inventorysqft, 100%—vacancy %, etc.), stock (e.g., inventory square footage(Office, Retail, Industrial) or units (Multifamily), revenue (e.g.,revenue generated by renting out or otherwise using a piece of realestate), savings (e.g., tax savings, depreciation), costs (e.g., taxes,insurance, upkeep, payments to property managers, costs for findingstenants, property managers, etc.), geography and geographic location(e.g., views of water, distance to shopping, walking score, proximity topublic transportation, distance to highways, proximity to job centers,proximity to local universities, etc.), building characteristics (e.g.,date built, date renovated, etc.), property characteristics (e.g.,address, city, state, zip, property type, unit type(s), number of units,numbers of bedrooms and bathrooms, square footage(s), lot size(s),assessed value(s), lot value(s), improvements value(s), etc.—possiblyincluding current and past values), real estate markets characteristics(e.g., local year-over-year growth, historical year-over-year growth),broader economic information (e.g., gross domestic product growth,consumer sentiment, economic forecast data), local economic information(e.g., local economic growth, average local salaries and growth, etc.),local demographics (e.g., numbers of families, couples, single people,number of working-age people, numbers or percentage of people with atdifferent education, salary, or savings levels, etc.). The techniquesherein may be used to train a real estate computer-based reasoning modelbased on previous properties. Once the real estate computer-basedreasoning system has been trained, then input properties may be analyzedusing the real estate reasoning system. Using the techniques herein, thesurprisal and/or conviction of the training data can be used to build anreal estate computer-based reasoning system that balances the size ofthe computer-based reasoning model with the information that eachadditional property record (or set of records) provides to the model.

The techniques herein may be used to predict performance of real estatein the future. For example, based on the variables associated discussedhere, that are related, e.g., with various geographies, property types,and markets, the techniques herein may be used to find property typesand geographies with the highest expected value or return (e.g., asCAGR). As a more specific example, a model of historical CAGR withasking rent, capitalization rate, demand, net absorption, netcompletions, occupancy, stock, etc. can be trained. That model may beused, along with more current data, to predict the CAGR of variousproperty types and/or geographies over the coming X years (e.g., 2, 3,5, or 10 years). Such information may be useful for predicting futurevalue for properties and/or automated decision making.

As another example, using the techniques herein, a batch of availableproperties may be given as input to the real estate computer-basedreasoning systems, and the real estate computer-based reasoning systemmay be used to determine what properties are likely to be goodinvestments. In some embodiments, the predictions of the computer-basedreasoning system may be used to purchase properties. Further, asdiscussed extensively herein, explanations may be provided for thedecisions. Those explanation may be used by a controllable system tomake investment decisions and/or by a human operator to review theinvestment predictions.

In some embodiments, processes 100, 400 may include determining thesurprisal and/or conviction of each input real estate data case (ormultiple real estate data cases) with respect to the associated labelsor of the aspects of the computer-based reasoning model. The surprisaland/or conviction for the one or more real estate data cases may bedetermined and a determination may be made whether to select or includethe one or more real estate data cases in the real estate computer-basedreasoning model based on the determined surprisal and/or conviction.While there are more sets of one or more real estate data cases toassess, the process may return to determine whether more real estatedata case sets should be included or whether aspects should be includedand/or changed in the model. Once there are no more training cases toconsider, the process can turn to controlling predicting real estateinvestments information for possible use in purchasing real estate usingthe real estate computer-based reasoning.

In some embodiments, process 100 may determine (e.g., in response to arequest) synthetic data for use in the real estate system computer-basedreasoning model. Based on a model that uses the synthetic data, theprocess can cause 199 control of a real estate system, using, forexample, process 400. For example, the training data elements arerelated to real estate, and the real estate computer-based reasoningmodel trained on that data will determined investment value(s) for realestate data cases (properties) under consideration. These investmentvalues may be any appropriate value, such as CAGR, monthly income,resale value, income or resale value based on refurbishment or newdevelopment, net present value of one or more of the preceding, etc. Insome embodiments, process 400 begins by receiving 410 a real estatecomputer-based reasoning model. The process proceeds by receiving 420properties under consideration for labeling and/or predicting value(s)for the investment opportunity. The real estate computer-based reasoningmodel is then used to determine 430 values for the real estate underconsideration. The prediction(s) for the real estate is (are) then made440. If there are more 450 properties to consider, then the systemreturns to receive 410 data on those properties and otherwise ceases460. In some embodiments, the real estate computer-based reasoning modelmay be used to determine which training properties are “closest” (ormost similar) to the incoming property or property types and/orgeographies predicted as high value. The investment value(s) for theproperties under consideration may then be determined based on the“closest” properties or property types and/or geographies.

Cybersecurity

The processes 100, 400 may also be used for cybersecurity analysis. Forexample, a cybersecurity company or other organization may want toperform threat (or anomalous behavior) analysis, and in particular maywant explanation data associated with the threat or anomalous behavioranalysis (e.g., why was a particular event, user, etc. identified as athreat or not a threat?). The computer-based reasoning model may betrained using known threats/anomalous behavior and features associatedwith those threats or anomalous behavior. Data that represents neither athreat nor anomalous behavior (e.g., non-malicious access attempts,non-malicious emails, etc.) may also be used to train the computer-basedreasoning model. In some embodiments, when a new entity, user, packet,payload, routing attempt, access attempt, log file, etc. is ready forassessment, the features associated with that new entity, user, packet,payload, routing attempt, access attempt, log file, etc. may be used asinput in the trained cybersecurity computer-based reasoning system. Thecybersecurity computer-based reasoning system may then determine thelikelihood that the entity, user, packet, payload, routing attempt,access attempt, pattern in the log file, etc. is or represents a threator anomalous behavior. Further, explanation data, such as a convictionmeasures, training data used to make a decision etc., can be used tomitigate the threat or anomalous behavior and/or be provided to a humanoperator in order to further assess the potential threat or anomalousbehavior.

Any type of cybersecurity threat or anomalous behavior can be analyzedand detected, such as denial of service (DoS), distributed DOS (DDoS),brute-force attacks (e.g., password breach attempts), compromisedcredentials, malware, insider threats, advanced persistent threats,phishing, spear phishing, etc. and/or anomalous traffic volume,bandwidth use, protocol use, behavior of individuals and/or accounts,logfile pattern, access or routing attempt, etc. In some embodiments thecybersecurity threat is mitigated (e.g., access is suspended, etc.)while the threat is escalated to a human operator. As a more specificexample, if an email is received by the email server, the email may beprovided as input to the trained cybersecurity computer-based reasoningmodel. The cybersecurity computer-based reasoning model may indicatethat the email is a potential threat (e.g., detecting and thenindicating that email includes a link to a universal resource locatorthat is different from the universal resource location displayed in thetext of the email). In some embodiments, this email may be automaticallydeleted, may be quarantined, and/or flagged for review.

In some embodiments, processes 100, 400 may include determining (e.g.,in response to a request) the surprisal and/or conviction of one or moredata elements or aspects of the cybersecurity system. The surprisal orconviction for the one or more cybersecurity system data elements oraspects may be determined and a determination may be made whether toselect or include the one or more cybersecurity system data elements oraspects in a cybersecurity system computer-based reasoning model basedon the determined surprisal and/or conviction. While there are more setsof one or more cybersecurity system data elements or aspects to assess,the process may return to determine whether more cybersecurity systemdata elements or aspects should be included. Once there are no morecybersecurity system data elements or aspects to consider, the processcan turn to controlling the cybersecurity computer-based reasoningsystem using the cybersecurity system computer-based reasoning model.

In some embodiments, process 100 may determine (e.g., in response to arequest) synthetic data for use in the cybersecurity systemcomputer-based reasoning model. Based on a model that uses the syntheticdata, the process can cause 199 control of a cybersecuritycomputer-based reasoning system using process 400. For example, if thedata elements are related to cybersecurity system actions, then thecybersecurity system computer-based reasoning model trained on that datawill control the cybersecurity system (e.g., quarantine, delete, or flagfor review, entities, data, network traffic, etc.). Process 400 proceedsby receiving 410 a cybersecurity system computer-based reasoning model.The process proceeds by receiving 420 a context. The cybersecuritysystem computer-based reasoning model is then used to determine 430 anaction to take. The action is then performed by the control system(e.g., caused by the cybersecurity system computer-based reasoningsystem). If there are more 450 contexts to consider, then the systemreturns to receive 410 those contexts and otherwise ceases 460. In suchembodiments, the cybersecurity system computer-based reasoning model maybe used to assess cybersecurity threats, etc. In some embodiments, thechosen action(s) are then performed by a control system.

Example Control Hierarchies

In some embodiments, the technique herein may use a control hierarchy tocontrol systems and/or cause actions to be taken (e.g., as part ofcausing 199 control). There are numerous example control hierarchies andmany types of systems to control, and hierarchy for vehicle control ispresented below. In some embodiments, only a portion of this controlhierarchy is used. It is also possible to add levels to (or removelevels from) the control hierarchy.

An example control hierarchy for controlling a vehicle could be:

-   -   Primitive Layer—Active vehicle abilities (accelerate,        decelerate), lateral, elevation, and orientation movements to        control basic vehicle navigation    -   Behavior Layer—Programmed vehicle behaviors which prioritize        received actions and directives and prioritize the behaviors in        the action.    -   Unit Layer—Receives orders from command layer, issues        moves/directives to the behavior layer.    -   Command Layers (hierarchical)—Receives orders and gives orders        to elements under its command, which may be another command        layer or unit layer.        Example Data Cases, Data Elements, Contexts, and Operational        Situations

In some embodiments, the cases, data cases, or data elements may includecontext data and action data in context-action pairs. Further, cases mayrelate to control of a vehicle, control of a smart voice control, healthsystem, real estate system, image labelling systems, or any of the otherexamples herein. For example, context data may include data related tothe operation of the vehicle, including the environment in which it isoperating, and the actions taken may be of any granularity. Consider anexample of data collected while a driver, Alicia, drives around a city.The collected data could be context and action data where the actionstaken can include high-level actions (e.g., drive to next intersection,exit the highway, take surface roads, etc.), mid-level actions (e.g.,turn left, turn right, change lanes) and/or low-level actions (e.g.,accelerate, decelerate, etc.). The contexts can include any informationrelated to the vehicle (e.g. time until impact with closest object(s),speed, course heading, breaking distances, vehicle weight, etc.), thedriver (pupillary dilation, heart rate, attentiveness, hand position,foot position, etc.), the environment (speed limit and other local rulesof the road, weather, visibility, road surface information, bothtransient such as moisture level as well as more permanent, such aspavement levelness, existence of potholes, etc.), traffic (congestion,time to a waypoint, time to destination, availability of alternateroutes, etc.), and the like. These input data (e.g., context-actionpairs for training a context-based reasoning system or input trainingcontexts with outcome actions for training a machine learning system)can be saved and later used to help control a compatible vehicle in acompatible operational situation. The operational situation of thevehicle may include any relevant data related to the operation of thevehicle. In some embodiments, the operational situation may relate tooperation of vehicles by particular individuals, in particulargeographies, at particular times, and in particular conditions. Forexample, the operational situation may refer to a particular driver(e.g., Alicia or Carole). Alicia may be considered a cautious cardriver, and Carole a faster driver. As noted above, and in particular,when approaching a stop sign, Carole may coast in and then brake at thelast moment, while Alicia may slow down earlier and roll in. As anotherexample of an operational situation, Bob may be considered the “bestpilot” for a fleet of helicopters, and therefore his context and actionsmay be used for controlling self-flying helicopters.

In some embodiments, the operational situation may relate to theenvironment in which the system is operating. In the vehicle context,the locale may be a geographic area of any size or type and may bedetermined by systems that utilize machine learning. For example, anoperational situation may be “highway driving” while another is “sidestreet driving”. An operational situation may be related to an area,neighborhood, city, region, state, country, etc. For example, oneoperational situation may relate to driving in Raleigh, N.C. and anothermay be driving in Pittsburgh, Pa. An operational situation may relate tosafe or legal driving speeds. For example, one operational situation maybe related to roads with forty-five miles per hour speed limits, andanother may relate to turns with a recommended speed of 20 miles perhour. The operational situation may also include aspects of theenvironment such as road congestion, weather or road conditions, time ofday, etc. The operational situation may also include passengerinformation, such as whether to hurry (e.g., drive faster), whether todrive smoothly, technique for approaching stop signs, red lights, otherobjects, what relative velocity to take turns, etc. The operationalsituation may also include cargo information, such as weight,hazardousness, value, fragility of the cargo, temperature sensitivity,handling instructions, etc.

In some embodiments, the context and action may include systemmaintenance information. In the vehicle context, the context may includeinformation for timing and/or wear-related information for individual orsets of components. For example, the context may include information onthe timing and distance since the last change of each fluid, each belt,each tire (and possibly when each was rotated), the electrical system,interior and exterior materials (such as exterior paint, interiorcushions, passenger entertainment systems, etc.), communication systems,sensors (such as speed sensors, tire pressure monitors, fuel gauges,compasses, global positioning systems (GPS), RADARs, LiDARs, cameras,barometers, thermal sensors, accelerometers, strain gauges, noise/soundmeasurement systems, etc.), the engine(s), structural components of thevehicle (wings, blades, struts, shocks, frame, hull, etc.), and thelike. The action taken may include inspection, preventative maintenance,and/or a failure of any of these components. As discussed elsewhereherein, having context and actions related to maintenance may allow thetechniques to predict when issues will occur with future vehicles and/orsuggest maintenance. For example, the context of an automobile mayinclude the distance traveled since the timing belt was last replaced.The action associated with the context may include inspection,preventative replacement, and/or failure of the timing belt. Further, asdescribed elsewhere herein, the contexts and actions may be collectedfor multiple operators and/or vehicles. As such, the timing ofinspection, preventative maintenance and/or failure for multipleautomobiles may be determined and later used for predictions andmessaging.

Causing performance of an identified action can include causing acontrol system to control the target system based on the identifiedaction. In the self-controlled vehicle context, this may include sendinga signal to a real car, to a simulator of a car, to a system or devicein communication with either, etc. Further, the action to be caused canbe simulated/predicted without showing graphics, etc. For example, thetechniques might cause performance of actions in the manner thatincludes, determining what action would be take, and determining whetherthat result would be anomalous, and performing the techniques hereinbased on the determination that such state would be anomalous based onthat determination, all without actually generating the graphics andother characteristics needed for displaying the results needed in agraphical simulator (e.g., a graphical simulator might be similar to acomputer game).

Hardware Overview

According to some embodiments, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 3 is a block diagram that illustrates a computersystem 300 upon which an embodiment of the invention may be implemented.Computer system 300 includes a bus 302 or other communication mechanismfor communicating information, and a hardware processor 304 coupled withbus 302 for processing information. Hardware processor 304 may be, forexample, a general purpose microprocessor.

Computer system 300 also includes a main memory 306, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 302for storing information and instructions to be executed by processor304. Main memory 306 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 304. Such instructions, when stored innon-transitory storage media accessible to processor 304, rendercomputer system 300 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 300 further includes a read only memory (ROM) 308 orother static storage device coupled to bus 302 for storing staticinformation and instructions for processor 304. A storage device 310,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 302 for storing information and instructions.

Computer system 300 may be coupled via bus 302 to a display 312, such asan OLED, LED or cathode ray tube (CRT), for displaying information to acomputer user. An input device 314, including alphanumeric and otherkeys, is coupled to bus 302 for communicating information and commandselections to processor 304. Another type of user input device is cursorcontrol 316, such as a mouse, a trackball, or cursor direction keys forcommunicating direction information and command selections to processor304 and for controlling cursor movement on display 312. This inputdevice typically has two degrees of freedom in two axes, a first axis(e.g., x) and a second axis (e.g., y), that allows the device to specifypositions in a plane. The input device 314 may also have multiple inputmodalities, such as multiple 2-axes controllers, and/or input buttons orkeyboard. This allows a user to input along more than two dimensionssimultaneously and/or control the input of more than one type of action.

Computer system 300 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 300 to be a special-purpose machine. Accordingto some embodiments, the techniques herein are performed by computersystem 300 in response to processor 304 executing one or more sequencesof one or more instructions contained in main memory 306. Suchinstructions may be read into main memory 306 from another storagemedium, such as storage device 310. Execution of the sequences ofinstructions contained in main memory 306 causes processor 304 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 310. Volatile media includes dynamic memory, such asmain memory 306. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 302. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 304 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 300 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 302. Bus 302 carries the data tomain memory 306, from which processor 304 retrieves and executes theinstructions. The instructions received by main memory 306 mayoptionally be stored on storage device 310 either before or afterexecution by processor 304.

Computer system 300 also includes a communication interface 318 coupledto bus 302. Communication interface 318 provides a two-way datacommunication coupling to a network link 320 that is connected to alocal network 322. For example, communication interface 318 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 318 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 318sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.Such a wireless link could be a Bluetooth, Bluetooth Low Energy (BLE),802.11 WiFi connection, or the like.

Network link 320 typically provides data communication through one ormore networks to other data devices. For example, network link 320 mayprovide a connection through local network 322 to a host computer 324 orto data equipment operated by an Internet Service Provider (ISP) 326.ISP 326 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 328. Local network 322 and Internet 328 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 320and through communication interface 318, which carry the digital data toand from computer system 300, are example forms of transmission media.

Computer system 300 can send messages and receive data, includingprogram code, through the network(s), network link 320 and communicationinterface 318. In the Internet example, a server 330 might transmit arequested code for an application program through Internet 328, ISP 326,local network 322 and communication interface 318.

The received code may be executed by processor 304 as it is received,and/or stored in storage device 310, or other non-volatile storage forlater execution.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction.

What is claimed is:
 1. A method comprising: receiving a request forgeneration of synthetic training data based on a set of training datacases; determining one or more focal training data cases from among theset of training data cases; for each undetermined feature in the one ormore focal training data cases, determining a value for the undeterminedfeature in a synthetic data case based at least in part on the focaltraining data cases; determining whether the value for the undeterminedfeature is valid based on feature information; when it is determinedthat the value for the undetermined feature is invalid, performing acorrective action for the value of the feature; when it is determinedthat the value for the undetermined feature is valid, using the valuefor the undetermined feature; and causing control of a controllablesystem using a computer-based reasoning model that was determined atleast in part based on the synthetic data case; wherein the method isperformed by one or more computing devices.
 2. The method of claim 1,wherein determining whether the value for the undetermined feature isvalid based on feature information comprises: determining whether thevalue for the undetermined feature is valid based on a comparison of thevalue with feature bounds.
 3. The method of claim 2, wherein determiningwhether the value for the undetermined feature is valid based on featureinformation comprises: determining the feature bounds based on values ofone or more other features in the synthetic data case.
 4. The method ofclaim 2, wherein performing the corrective action comprises: determininga new value for the undetermined feature based on a distribution betweenthe determined feature bounds.
 5. The method of claim 1, whereinperforming the corrective action comprises: determining a new value forthe undetermined feature in the synthetic data case based at least inpart on a distribution associated with the undetermined feature in thefocal training data cases.
 6. The method of claim 1, wherein determiningthe value for the undetermined feature in the synthetic data casecomprises: determining the value for the undetermined feature based atleast in part on a distribution associated with the undeterminedfeature.
 7. The method of claim 1, wherein the controllable system is aself-driving vehicle, the method further comprising: receiving a currentcontext for the self-driving vehicle during operation of theself-driving vehicle; determining a suggested action for theself-driving vehicle based at least in part on the synthetic data caseand the current context for the self-driving vehicle; wherein causingcontrol of the controllable system comprises causing performance of thesuggested action by the self-driving vehicle.
 8. The method of claim 1,wherein the request includes a target amount of surprisal for thesynthetic training data; and wherein determining the value for theundetermined feature in the synthetic data case comprises determiningthe value for the undetermined feature in the synthetic data case basedat least in part on a distribution associated with the undeterminedfeature in the focal training data cases and the target amount ofsurprisal.
 9. The method of claim 8, wherein the request includes thetarget amount of surprisal for the synthetic training data; and whereindetermining, for each undetermined feature in the one or more focaltraining data cases, the value for the undetermined feature in thesynthetic data case comprises: determining a portion of the targetamount of surprisal attributable to the undetermined feature bysplitting surprisal evenly among the undetermined features, where eachof N undetermined features uses (target surprisal)/N amount of thesurprisal; and determining the distribution for the undetermined featurebased on the portion of the target amount of surprisal attributable tothe undetermined feature.
 10. The method of claim 1, further comprising:determining a fitness score for the synthetic data case; and when thefitness score for the synthetic data case is beyond a particularthreshold, using the synthetic data case as synthetic training data. 11.The method of claim 1, further comprising: determining a shortestdistance between the synthetic data case and cases in the set oftraining data cases; and when the shortest distance between thesynthetic data case and the cases in the set of training data cases isbeyond a particular threshold, using the synthetic data case assynthetic training data.
 12. The method of claim 1, further comprising:determining distances between the synthetic data case and at least twocases in the set of training data cases; determining whether there areat least a certain number (k) of training data cases that have adistance to the synthetic data case that is below a threshold; and whenthere are at least k training data cases that have distances to thesynthetic data case that are below the threshold, using the syntheticdata case as synthetic training data.
 13. The method of claim 1, furthercomprising: determining distances between the synthetic data case and atleast two cases in the set of training data cases; determining whetherthere are at least a certain number (k) of training data cases that havea distance to the synthetic data case that is below a first threshold;determining whether any of the training data cases have a distance belowa second threshold; and when there are at least k training data caseshave distances to the synthetic data case that are below the firstthreshold and no training data case has a distance to the synthetic datacase that is below the second threshold, using the synthetic data caseas synthetic training data.